A method for supporting the design of efficient heating systems using EU product policy data

Abstract

The design of heating, ventilation and air conditioning systems is still a challenging engineering task which requires experienced and informed decision-making. These systems have a great energy-saving potential at the system level rather than at the level of the individual products of which they are composed. European environmental product policies have been very useful in facilitating a homogeneous rating scheme which can be used to compare the energy performance of different products that provide the same service. This paper proposes a simplified design method  which uses the performance of components that are regulated by European product policies to obtain the overall performance of heating systems in residential buildings. It is a flexible method and allows different product configurations to be assessed so as to optimise the system performance during the design phase. The method is tested on a real case study with domestic hot water and space heating systems. The case study shows the potential for improving the heating systems according to the performance levels of its products currently available in the market. Results of the domestic hot water system show that upgrading its storage tank to the maximum energy class (A⁺) could bring the highest energy savings ( 4162 kWh/y).

Keywords:

• Design for environment
• energy-using products (EuP)
• environmental performance indicators
• heating system design
• product policies

1. Introduction

The building sector accounts for 40% of the total energy consumption in the European Union (EU) (EC 2011a). In 2012, half of the EU’s energy consumption (546 Mtoe) was due to heating and cooling, and much of this was wasted through insufficient insulation or inefficient equipment in buildings, among others (EC 2016). Greater energy efficiency in new and existing buildings is crucial in order to reach the goal of the European Commission’s energy roadmap for reducing the GHG emissions by 80–95% by 2050 compared to 1990 (EC 2011b). In the EU, the implementation of the Energy Performance of Buildings Directive (EPBD) (EC 2010b) promotes energy efficiency by reducing the amount of energy consumed to maintain the indoor environment through heating, cooling, lighting, operating appliances and the use of renewable energy in buildings. Moreover, the Ecodesign and Energy Labelling Directives (EC 2009, 2010a) promote the production and consumption of more energy-efficient products. Typical energy-using products used in buildings (e.g. boilers) have already been regulated for many years. The review of the Ecodesign Directive in 2009 extended its scope to include Energy-related Products (ErP), addressing other relevant building products (e.g. windows, taps, showers, insulation components). Although policies already co-exist at the macro- (i.e. buildings, through the EPBD) and micro- (i.e. building components, through the Ecodesign and Energy Labelling Directives) levels, there is still a technological gap between building designers and regulators that needs to be filled in order to ensure the achievement of overall energy efficiency objectives (Allouhi et al. 2015).

2. Literature review

Heating, ventilation and air conditioning (HVAC) systems account for 50% of the total energy consumption of buildings (Pérez-Lombard et al. 2008). The design of efficient HVAC systems is a huge challenge since buildings are complex systems, composed of many and very heterogeneous materials and devices that interact with each other, the outside environment and their inhabitants (Peuportier, Thiers, and Guiavarch 2013). The performance of complex systems is dynamic and changes according to conditions of use, maintenance, component upgrading, etc. (Tchertchian, Dominique Millet, and Yvars 2016). However, the decisions made in building design phase (Annunziata et al. 2016) and in particular on the components chosen for the system are crucial to avoid major environmental impacts.

When designing HVAC systems, the choice of the performance of the products to be installed is usually made with regard to load calculations (Harish and Kumar 2016). The optimisation of building design is still a topic of research and has yet to be implemented in engineering (Attia et al. 2013). Thus, in order to improve the energy performance of residential buildings, the building needs to be considered as a whole rather than as its individual components, and the solutions should be more flexible and user-friendly than those currently used (De Boeck et al. 2015). The usual design procedure of HVAC systems focuses mainly on satisfying energy/heat demands, while system (or sub-system) optimisation which allows predicting performances is considered secondary (Randaxhe, Lemort, and Lebrun 2015; Attia et al. 2013; Reddi et al. 2012). System optimisation can be achieved at two different levels, in terms of energy efficiency performance and of low-emission performance (Fesanghary, Asadi, and Geem 2012). In addition, Ecodesign and Energy Labelling requirements for space and water heaters are expected to bring annual energy savings of 600 TWh and CO₂ emission reductions of 135 million tonnes by 2030 (EC 2016). These savings and CO₂ reductions could be even greater using the system approach. This paper aims to propose a general engineering method that supports design decisions for the optimisation of heating systems (the main part of HVAC systems) in residential buildings.

Simulation tools have been used in the past 40 years to integrate multiple aspects of system design (Colledani et al. 2014; Ellis and Mathews 2002) based on technical and usage performance (Cor et al. 2014) or on energy consumption (Bonvoisin et al. 2013), among others. Building simulation tools can precisely model HVAC systems but fail when they cannot be fed with enough and adequate data in the early design stages, and deliver useful results quite late in the design process. These tools require product parameters that often are not supplied by manufacturers. They are time-consuming and some are expensive. In addition, despite the increased number of improvements in simulation tools, there can still be up to 40% difference between predicted and real energy consumption in buildings (Trčka and Hensen 2010). Thus, some loss of accuracy might be acceptable if the design process could be sped up. Simplified tools such as conceptual system design and the use of simple equations require less input data, lower user expertise and yield more easily interpreted results. Trčka and Hensen (2010) stated that a combination of HVAC simulation tools with conceptual design at hand could be useful in system modelling since the advantages of the former match well with the flexibility of the latter. Simulation tools could be accompanied by simplified design tools earlier in the design process to be able to give useful and quicker information for practical decision-making. The combination of complex and simple tools is often used in the environmental impact assessments of different HVAC solutions (Zambrana-Vasquez et al. 2015; Yang, Zmeureanua, and Rivard 2008). The main assumptions of this paper are that the design phase must be supported and that simplified methods are part of the solution.

The method proposed in this paper addresses two difficulties that professionals face regarding the choice of product performance: the rapid evolution of technology which hinders the dynamic and up-to-date knowledge of markets; and how to combine different levels of product performance to obtain an optimal solution at system level.

When technology evolves very quickly, some products are improved as others become obsolete over a short period of time. Building designers therefore need to be continuously updated on the current market availability of products. In this regard, four European environmental product policies (the Ecodesign and Energy Labelling Directives (EC 2009, 2010a), the Green Public Procurement (GPP) (EC 2008b) and the EU Ecolabel (EC 2013a)) have facilitated the disclosure of very relevant information regarding product performance (Calero-Pastor, Mathieux, and Brissaud 2014). While the Ecodesign Directive sets minimum performance thresholds, the different energy classes reflect the intermediate variety of product performance levels currently available on the market, and GPP and the EU Ecolabel represent excellence in the performance of products. Thus, this batch of EU product policies could be seen as a mirror of the current market characteristics. The common goal of these product policies is to make the EU market more sustainable (EC 2008a). Indeed, they have been very successful in improving the energy efficiency of building products, especially those involved in HVAC systems such as boilers, space heaters, coolers and air circulators. According to these product policies, manufacturers and/or importers have to provide information regarding the performance of the products they put on the EU market. The methods used to measure the performance and the associated thresholds (updated regularly) of the product groups are usually developed during the ‘Preparatory Studies’, taking into account the currently or soon-to-be available technologies on the European market. The performance of such products is measured using agreed methods so that different products providing the same service can be verified and compared. In addition, customers and users are provided with homogenous and easy-to-understand ratings methods. The method proposed in this paper relies as much as possible on the methods used to calculate the energy efficiency of products developed by the European Commission (EC) and recognised by stakeholders (industry, government, consumer organisations, etc.).

This paper proposes and tests a simplified method to support the design process of efficient heating systems in residential buildings. It focuses on heating systems since most HVAC systems in residential buildings only provide water and space heating (SH; Perez-Lombard et al. 2008). The design method allows the energy performance of the heating system to be assessed according to the performance of its components. The method provides two new aspects that are not yet covered by the literature:

(1)	it allows the assessment of heating systems grounded on well-known and proven labelling schemes such as EU product policies, which are available at the early design stage and implemented by all manufacturers, and
(2)	it supports design activities at system level, providing informed decision-making on multiple design solutions based on different configurations of products with performance levels currently available on the market.

The requirements of the method can be summarised as:

•	is useful in the design phase;
•	facilitates decisions at system level;
•	uses easily accessible product information;
•	allows engineers to use their preferred alternative methods when appropriate; and
•	helps compare solutions.

Section 3 details the method while Section 4 describes how it was used in a real case study which includes a water heating system that includes solar devices and a SH system. The method is discussed in Section 5, and conclusions and outlook are given in Section 6.

3. Method for supporting the design of heating systems

The aim of the method is to support the design of heating systems in residential buildings. It should facilitate the selection of the most adequate quality components of the heating system that optimises its energy performance.

3.1. Global context and system modelling

The performance of a heating system depends on the performance of its components, its interactions with the building, the geographic context (climatic data, local conditions of the building, etc.) and user behaviour. The geographic context, the building envelope and user behaviour define the demand for energy services. The method recommends that all these variables be accurately taken into consideration. The method focuses on the products that make up the system and their best configuration in order to optimise system performance. The calculation of the demand for energy services will not be discussed, as the boundaries of this research are at the level of the heating system.

The purpose of the heating system is to provide domestic hot water (DHW) or SH to the dwelling. The heating system is composed of components with different functionalities (CEN 2006): energy generation, storage, distribution, delivery of the service and controls (Figure 1). At this point, it is also important to define how the components of the system are or can be connected, their sequence and main heat flows among them.

Figure 1. Heating system within the global context.

3.2. The method

The method aids calculate the energy performance of the heating system according to the performance of its components using EU product policy data. The valuable information that EU product policies provides on the energy efficiency of ErP products helps to:

(1)	lean on a reliable and agreed scheme that is already available at the design step and useful in making fair comparisons of products,
(2)	assess system performance based on the different performance levels of products currently available on the market and
(3)	analyse the possible alternatives regarding the combined performance of the products that make up the system.

However, the sequence of steps proposed in this method for calculating the energy parameters of a heating system does not differ from these applied in other building simulation tools.

The method assesses the energy performance of the heating system using four energy parameters: energy services demand, energy losses, non-renewable energy (NRE) consumption and low-emission energy efficiency. The method is summarised in Table 1.

Table 1. Overview of the design method.

Step 0: Global context and system modelling

Step 1: Calculation of the energy services demand

Step 2: Calculation of the energy losses and non-renewable energy (NRE) consumption

2A. Estimation of the product performance using available figures of EU product policies and/or, if necessary, calculating them with other tools

2B. Calculation of the energy losses and NRE consumption from the figure of the energy services demand

Step 3: Calculation of the low-emission energy efficiency

Step 0: Global context and system modelling (Section 3.1). The system is placed in a particular context of climate conditions, building envelope and user behaviour. The components of the heating system are then modelled, implementing the sub-functions needed as seen in Figure 1 and the relationships among them.

Step 1: Calculation of the energy services demand of the dwelling (E_{Services Demand}). Professionals calculate the energy services demand using simulation software (eQUEST, DesignBuilder, etc.), simple equations or even rules of thumb. Simulation tools are able to model the building envelope (closures, thermal bridges, etc.), the climatic data of the location and the user behaviours to obtain the energy services demand of a dwelling. Simple equations could refer the energy demand to, for instance, floor area, number of inhabitants or consumption patterns. Another option is to use available figures on the energy demand of the dwelling, for example the energy certifications of buildings according to the EPBD (EC 2010b).

Step 2: Calculation of the energy losses and the NRE consumed by the heating system (ENRE Consumption). The method supports the combination of EU product policies with other tools to assess the energy performance of a component.

Step 2A includes the compilation (or calculation) of the performance of each component or sub-system using data from EU product policies. The originality of the method lies in the outcome of this step.

Designers need to spend some time collecting data at all steps of the design process. When data are not available, the estimation might be more or less accurate depending on the time and effort the designer is willing to invest. Information on components covered by EU product policies such as the Ecodesign and Energy Labelling Directives, the GPP and the EU Ecolabel is available to designers. Technical information about products is available either in such regulations or through the technical documentation of the product provided by the manufacturer. Once the data-set is available, rules of thumb and simple equations provide results from very few macro-data. Simulation software needs more detailed and numerous data but facilitates results that are closer to reality and can estimate the effects of innovative solutions where local knowledge is missing.

In the method, product performance figures are compiled (or calculated) following EU product policies: either from real products (manufacturer’s technical information) or from the regulations affecting the target product (implementing regulations on the Ecodesign or supplementing regulations on the Energy Labelling Directives, the EU GPP or the Ecolabel for specific product groups). If a component or sub-system does not fall within the scope of such product policies, then its performance can be calculated using other tools such as simulation tools, simple equations and rules of thumb.

Step 2B calculates the energy losses and the NRE consumption based on the energy services demand calculated in step 1 (see Figure 2). The performance of each component (η_Comp) can be used in Equation 1(1) to calculate its energy losses (L_Comp) in Equation 2(2) , or the other way around. The energy losses of all components are aggregated to the E_{services Demand} in the opposite direction to the energy flow used to calculate the E_{NRE Consumption} (Equation 3(3) ).

Figure 2. Energy flows of the energy consumed by the system.

Where:

(1)

(2)

(3)

In conclusion, the energy losses of each component and the NRE consumption of the system are calculated in step 2.

Step 3: The low-emission energy efficiency of the system (η_SYSTEM) is then estimated from figures calculated in steps 1 and 2. The low-emission energy efficiency of the system is defined as:

(4)

This indicator does not include the RE consumption and shows the efficiency of the system with the aim of minimising its NRE (which causes major atmospheric emissions) consumption. Only the NRE consumption is considered since building-related policies are oriented towards low-emission designs. This has been an effective way in which policies have rewarded RE sources. In addition, the method recommends that the losses be analysed at the system level in order to analyse the optimisation of the system independently of the type of energy used (RE or NRE). This way, the minimisation of the energy losses of each component is also included.

4. Case study: DHW solar system and SH system

The aim of this section is to present the implementation of the method proposed in a real case study. It shows how the steps of the method are applied, which data are used and which results are produced. The case study includes a DHW system and a SH system. The heating systems are redesigned in order to identify their most significant improvement potential.

4.1. Calculation of energy performance parameters based on the proposed method

4.1.1. Step 0: global context and system modelling

The house is located in the North of Italy and the dwelling has a floor surface of 61 m². The house and its heating systems were refurbished in 2012. The DHW system installed in the dwelling consists of a solar panel with a glycol pump, a storage tank with two coils, a natural gas boiler, a sanitary water pipe network, three taps and one shower (Figure 3). The SH system includes the same boiler, distribution components, underfloor heating and temperature control. There are also components such as an expansion vessel, a mixer valve and a safety valve that are not represented in Figure 3 since their energy losses are considered to be negligible.

Figure 3. DHW and SH systems of the case study.

The components and sub-systems of the heating systems (numbered from 1 to 8) are grouped according to their function in the overall system (Table 2).

Table 2. System modelling of the heating systems.

	Generation	Storage	Distribution	Emission	Controls
DWH system	2. Solar sub-system	3. Storage tank	4. Distribution components DWH	5. Taps and showers	–
	1. Boiler				–
SH system
	–	–	6. Distribution components SH	7. Underfloor heating	8. Temperature control

Table 3. Performance of the system components of the case study.

Systems	Sub-system and components	Performance (current design)	Performance calculation
DHW system	2. Solar sub-system	Solar contribution: 64.5%	SEAS3 solare (ENEA 2014).
	3. Storage tank	Standing losses: 226 W	Calculated from the figure of the thermal dispersion declared by the manufacturer. Energy label G (EC 2013b)
		Total losses (LST): 2070 kWh/y	Calculated with SEAS3 (monthly calculation according to climate data)
	4. Distribution components DHW	Total losses (LDIST): 1018 kWh/y	Calculated with SEAS3
	5. Taps & showers	No energy losses, no savings (on the DHW energy demand)	Case study assumption
	1. Boiler	Water heating energy efficiency (ηDHW Boi): 74.4% (load profile M)	Compiled from manufacturer’s product sheet. Energy label A for both functions (EC 2013e)
SH system		Seasonal space heating energy efficiency (ηSH Boi): 92%
	6. Distribution components SH	Losses (LSH Dist): 38 kWh/y	Calculated with SEAS3.
	7. Underfloor heating	ηUFloor = 97%	SEAS3 (default value).
	8. Controls	Temperature control: Class V: savings of 3% (SCont)	Assumption based on information included in Energy Labelling (EC 2014b).

Table 4. Energy flows (kWh/y) of the case study.

DHW system
Distribution components DHW		Storage tank		Boiler DHW
LDHW Dist (Table 3)	EDHW Dist INPUT (Equation 2(2) )	LST (Table 3)	EST INPUT (Equation 2(2) )	EDHW Boi OUTPUT (Equation 5(5) )	EDHW Boi INPUT (Equation 1(1) )
1018	1655	2070	3725	3326	4471
SH system
Underfloor heating		Distribution components SH		Controls	Boiler SH
EUFloor INPUT (Equation 1(1) )	LUFloor (Equation 2(2) )	LSH Dist (Table 3)	ESH Dist INPUT (Equation 1(1) )	ESH Boi OUTPUT (Equation 6(6) )	ECont (ESH Boi INPUT*SCont)	ESH Boi INPUT (Equation 1(1) )
18,630	545	38	18,668	18,108	560	19,683

Table 5. Summary of energy parameters (in kWh/y) of the current design of the case study.

DHW system
EDHW Demand	EDHW Boi INPUT (Table 4)	ηDHW SYSTEM (Equation 7(7) )	EHW Losses (∑LDHW Comp (i))
637	4471	14.2%	4233
SH system
ESH Demand	ESH Boi INPUT (Table 4)	ηSH SYSTEM (Equation 8(8) )	ESH Losses (∑LSHComp (i))
18,085	19,683	91.9%	2158

Table 6. Share of energy losses of the components of the heating systems of the case study.

	3. Storage tank	4. Distribution components DHW	1. Boiler DHW	1. Boiler SH	6. Distribution components SH	7. Underfloor heating
kWh/y	2070	1018	1141	1575	38	545
Share (%)	32	16	18	25	1	8

4.1.2. Step 1: calculation of the demand for energy services

Given the number of two dwelling inhabitants and considering an average consumption of 50 L/person/day, the simulation tool SEAS3 (ENEA 2014) recommended by the Italian Energy Agency calculated the annual DHW energy demand (E_{DHW Demand}) at 637 kWh/y. The monthly average solar contribution is 64.5% of the total DHW energy demand, which corresponds to 399 kWh/y, based on climatic data (calculated with SEAS solare, complementary software to SEAS3). The non-solar energy demand provided by the boiler (E_{Boi Non-solar}) corresponds to 238 kWh/y. The annual energy demand for SH (E_{SH Demand}) is 18,085 kWh based on climate conditions, energy losses from the building envelope and user behaviour (SEAS3 simulation).

4.1.3. Step 2: calculation of energy losses and the NRE consumption

Step 2A. According to the manufacturers’ declarations, the installed boiler has an energy label A for both DHW and SH systems, according to EC (2013e), with a water heating energy efficiency of 74% and a seasonal SH energy efficiency of 92%. The solar device is indirectly included within the scope of EC (2013b), in the packages of water heaters and solar device. The storage tank has an energy label G (226 W of standing losses), according to EC (2013b). The annual energy losses of the storage tank are calculated using SEAS3 based on the figure of the thermal dispersion declared by the manufacturer (5.03 W/K) and climate data (external temperature). The annual energy losses of the distribution of the DHW and SH systems were assessed using SEAS3 based on data regarding the installed technology (length of pipes and insulation). Taps and showers have a direct influence on the DHW energy demand. The taps and showers used in the dwelling correspond to average market products and thus it is assumed that no significant energy losses or savings occur on the DHW energy demand (see case study assumption Table 7). The efficiency of the underfloor heating is 97%, the default value given by SEAS3 for this type of SH delivery. The temperature control, which is indirectly included in the Energy Labelling of packages of space heater and temperature control (EC 2013e), is a control class V and contributes to 3% of the seasonal SH efficiency of the package. It is assumed that the same percentage of savings (3%) is achieved in the boiler for SH due to the temperature control.

Table 7. Performance levels of the system components.

Variables (component parameters)	Current design (case study system)	Performance levels	Source/comments
DHW System
2. Solar panels (number of panels)	1	0, 1 (2.06 m^2), 2 (4.12 m^2) solar panels	Same characteristics of the one already installed
1. Boiler (energy class/water heating energy efficiency)	A (74.4%)	E (30–33%), D (33–36%), C (36–39%), B (39–65%), A (65–100%)	See Table 8
3. Storage tank (energy class/ standing losses)	G (226 W)	G (>158 W), F (158–130 W), E (130–100 W), D (100–80 W), C (80–57 W), B (57–41 W), A (41–30 W), A+ (<30 W)	Energy labelling (EC 2013b)
5. Taps and showers (energy losses/savings)	0% losses	−53%, −35% and −18% losses	Assumption based on Swedish label SS 820000:2010 (SIS 2010).
	0% savings	18%, 35%, 53% savings
SH SYSTEM
1. Boiler (energy class/ seasonal SH energy efficiency)	A (ηSH Boi 92%)	B (82–90%), A (90–96%)	Energy Labelling (EC 2013e), Ecodesign (EC 2013d) and EU Ecolabel (EC 2014b).
8. Controls	Class V: 3% savings	No controls (0% savings), class I (1% savings), class III (1.5% savings), class II and IV (2% savings), class VII (3.5% savings), class VI (4% savings), class VIII (5% savings)	Assumption made based on energy labelling (EC 2013e).

Step 2B. Figure 4 shows the energy flows from one component to the next.

Figure 4. Energy flow chart of the heating systems of the case study.

The NRE consumption is the energy that needs to enter the boiler to provide both DHW and SH functions. Energy flows and losses are calculated monthly according to Equations 1(1) and 2(2) for each component, respectively. However, Table 4 provides annual figures (kWh/y). For the DHW system, since no losses or saving are assumed in the installed taps and showers, in the current design E_{Dist OUTPUT}=E_{Taps INPUT}=E_{HW Demand}= 637 kWh/y. The energy provided by the boiler for DHW (E_{DHW Boi OUTPUT}) is the energy input into the storage tank (E_{ST INPUT}) minus the energy provided by the solar sub-system (E_{Sol OUTPUT}) (Equation 5(5) ).

(5)

In the SH system, the energy demand is satisfied only through the boiler. E_{SH Boi OUTPUT} can be calculated using Equation 6(6) .

(6)

4.1.4. Step 3. calculation of low-emission energy efficiency

The low-emission energy efficiency of the DHW system is, according to Equation 4(4) , the ratio between the DHW demand (E_{DHW Demand}) and the energy required by the boiler for producing DHW (E_{DHW Boi INPUT}).

(7)

The low-emission energy efficiency of the SH system is, according to Equation 4(4) , the ratio between the SH demand (E_{SH Demand}) and the energy input into the boiler for SH (E_{SH Boi INPUT}).

(8)

Results in Table 5 show that, despite the high energy demand of the SH system, this behaves better than the DHW system in terms of energy efficiency and total energy losses. Thus, the DHW system has a higher potential for improvement.

The importance of the energy losses of each component (Table 6) gives an overview of how a component behaves within the overall system, regardless the type of energy used (RE or NRE).

Combining its DHW and SH losses, the boiler is the component with the highest losses (43%), despite its rather good performance (Table 3). According to the results shown in Table 6, the components that contribute the most to the total losses are the boiler (especially the SH function) and the storage tank (joint share of 75%).

4.2. Choosing the most adequate combination of components for the system design

4.2.1. Improvement potential of each product

A first analysis is carried out to study the improvement potential of individual products in terms of their system energy-saving potential (Table 7 and Figure 5).

Figure 5. Results of the analysis of individual components’ potential for improvement.

The performance ranges of the boiler, the storage tank, the taps and showers and the controls were assessed according to their respective regulations or by making assumptions when needed (see last column of Table 7). Table 8 presents, as an example, the full range of energy classes of the water heating function of the boiler, according to EC (2013b). Note that, according to this scale, the boiler of the current design has an energy class A.

Table 8. Performance range of the water heating energy efficiency of the boiler.

Energy labelling (EC 2013e)		Other product policies
Energy efficiency class	Water heating energy efficiency – Profile M (%)
A+++	ηWH ≥ 163	In theory, these energy classes can only be achieved in packages of water heaters that include also solar devices (Van Amerongen 2015)
A++	130 ≤ ηWH < 163
A+	100 ≤ ηWH < 130
A	65 ≤ ηWH < 100	74.4% Current boiler case study
		65% EU Ecolabel (EC 2014a)
B	39 ≤ ηWH < 65	–
C	36 ≤ ηWH < 39	–
D	33 ≤ ηWH < 36	–
E	30 ≤ ηWH < 33	30% – minimum Ecodesign requirements (EC 2013e)
F	27 ≤ ηWH < 30	Phase out by Ecodesign (EC 2013d)
G	ηWH < 27	Phase out by Ecodesign (EC 2013d)

Although taps and showers are regulated by EU Ecolabel and GPP criteria, these product policies do not provide a quantifiable measure of the energy consumption associated with these components. Instead, the Swedish Standard 820000:2010 (SIS (Swedish Standards Institute) 2010) provides an energy classification for different levels of energy use for mechanical basin and mixing valves. We use this to generate better and worse scenarios of the case study, modifying the DHW energy demand. Therefore, we assume that taps and showers that are below the average performance level (current design of the case study) generate energy losses, and taps and showers that are above the average performance level generate energy savings in the same amounts as in SIS (2010). The distribution components and the underfloor heating are not included in Table 7 since they cannot be easily replaced (they are embedded in the building structure and the building was recently refurbished). However, the performance of the current design of the distribution and underfloor heating has been used in the analysis (Table 3).

Figure 5 has been built based on every performance level of each individual component (Table 7). Based on the current design, the performance level is modified one at a time and the rest of the components’ performances are left as in the current design. Thus, Figure 5 shows results of 38 heating systems: 26 (3 + 9 + 7 + 7) DHW systems and 12 (4 + 8) SH systems. These results show, for each component, the system potential for improvement expressed in energy savings (kWh/y). Improvement would be negative if, for example, the current boiler (labelled A) were replaced by a worse technology (energy classes from B to E). Therefore, upgrading the storage tank to the maximum energy class (A⁺) could bring the highest energy savings to the DHW system (up to 4162 kWh/y). An upgrade of the SH function of the boiler could lead to energy savings of 1012 kWh/y in the system. Efficient taps and showers could lead to savings of 985 kWh/y in the DHW system. Using controls of class VIII could lead to savings of 748 kWh/y. The DHW function of the boiler (243 kWh/y) and the solar panels (48 kWh/y) have less significant potential for improvement.

4.2.2. Combinations of components’ performance levels

A second analysis focuses on how to combine the different levels of performance of the components in order to optimise the whole system. Only the results of the DHW system are presented here. Assumptions have been made regarding the DHW system to generate design options that combine different levels of performance of components:

(1)	The water heating energy efficiency of the boiler could be improved up to 100%. Two A-labelled boilers are considered (74 and 100%, respectively);
(2)	As the number of solar panels has poor potential for improvement (Figure 5), only one panel is considered in the following;
(3)	The storage tank could be easily improved up to the minimum value of energy class B (57 W) since this class represents the average products in the market (Van Amerongen 2015). Six energy classes are considered: G (current design 226 W), F, E, D, C, B;
(4)	As distribution components cannot feasibly be improved since the design of the building hinders the possibility to use less tubing (the kitchen is located far from the bathroom), their improvement has not been considered (only the current design, LST = 1018 kWh/y);
(5)	Regarding taps and showers, four levels have been considered (0, 18, 35 and 53% of savings on the EDHW Demand).

Given these assumptions, there are 48 possible DHW systems (2 × 1 × 6 × 1 × 4). Figure 6 shows the NRE consumption for 32 (2 × 1 × 4 × 1 × 4) design options; for simplification of the figure, two levels of performances for the storage tanks (C and B) are not presented. Each quartet of bars represents a combination of a boiler (74 and 100% of water heating energy efficiency) and a storage tank (from G to B energy class). The colour of each bar corresponds to the four different levels of efficiency of the taps and showers considered (0% savings in blue, 18% savings in orange, 35% savings in grey and 53% savings in yellow).

Figure 6. Alternative solutions based on combining products with different levels of performance.

According to Figure 6, for an energy-savings system target (with respect to the current design), the designer could choose among various design options (DO) or combinations of products with different performance levels. For instance, achieving a system’s energy saving of at least 10–30%, the taps and showers need to be replaced by ones that lead to 18% energy savings on the energy demand (DO1). Other options include choosing more efficient taps and showers (DO2 and DO3) or replacing the boiler by one with 100% of water heating energy efficiency (DO4). To achieve system savings of at least 30–50%, the boiler must be substituted by one with 100% of water heating energy efficiency, and taps and showers must be replaced by others that lead to 18% savings on energy demand (DO5). Another option could be to upgrade the storage tank to an F energy class and replace the taps and showers by ones that save 18% of the energy demand (DO6).

4.2.3. Economic analysis of the design options of the DHW system

An economic analysis has been carried out to illustrate how results concerning energy savings of the DO presented in the previous section can be combined with other design criteria. The life cycle costs have been calculated by adding the investment costs to the present values of the operation costs during a 20-year lifetime as proposed by Zambrana-Vásquez et al. (2015). The investment costs were the ones of the real product purchases and their installation of all the DHW system equipment. The discount rate considered is 2.4%. It has been calculated with the inflation and interest rates, according to the analysis of the evolution of prices in previous years in Italy (based on Eurostat). The cost of the natural gas is 0.0776 €/kWh with an annual growth rate of 4%, according to the analysis of the gas prices for domestic consumers of previous years in Italy (Eurostat 2016). The water price is the current one in Lombardy (1.287 €/m³) with an annual growth rate of 2.5% (EC – JRC 2014). All costs exclude VAT. The purchase costs of the better boiler and storage tanks are based on real prices of the Italian market. For the better taps and showers, it has been assumed the installation of aerators and flow regulators (EC and AEA 2011) to one tap of the dwelling (savings 18%), to two taps of the dwelling (savings of 35%) and to two taps and one shower of the dwelling (savings of 53%). The prices of these technologies and their substitution rate are European averages (EC – JRC 2014) and they have been included in the operation costs.

Results of the economic analysis are shown in Table 9. Cost savings of DO1 and DO2 are low (2.3 and 5.5%) while DO3, DO4, DO5 and DO6 achieve greater savings (9.3, 8.9, 13.2 and 14.7%, respectively). Although DO4 achieves more energy savings than DO3, its life cycle costs are higher. This is due to the lower costs of the modification of taps and showers in DO3 than the replacement of the boiler in DO4, and because the NRE and water consumption of DO3 are much lower than that in DO4. Designers should find a compromised solution between the energy and cost savings. Results also show that slightly lower energy consumption in DO6 than in DO5 can also bring significant economical benefits to the users. On the other hand, it is more convenient to replace the storage tank up to an F energy class than a boiler with 100% of water heating energy efficiency since the investment costs are lower and still their energy performance very similar. These results show that the set of indicators on energy performance calculated through the new method can be advantageously combined with economic indicators to support informed decision-making by designers.

Table 9. Economic results of the DHW system of the case study (all values are in €).

	Current DO	DO1	DO2	DO3	DO4	DO5	DO6
Investment costs (products + installation)	5990	5990	5990	5990	7592	7592	7388
Operation costs (natural gas + sanitary water + maintenance and repair)	18,755	18,181	17,400	16,447	14,938	13,884	13,714
Total	24,745	24,170	23,390	22,437	22,530	21,476	21,102
Savings with respect the current design	–	−574	−1355	−2308	−2215	−3268	−3643

5. Discussion

5.1. Limits and advantages of the method

The method proposed estimates the energy performance of a heating system based on the performance of its elements (components and sub-systems), using data from EU product policies. The method calls for the decomposition of the system into elements and the aggregation of the performance of each element. Theoretically, the granularity of the decomposition does not matter – all that is required is the possibility to link each element with its performance. In practice, the decomposition is an expert task undertaken by a senior designer. The proposed method adapts to the most appropriate level of decomposition to manage interdependencies among elements. However, in reality, the behaviour of the system is not a simple combination of the behaviours of its elements. The proposed method is valid only if the behaviour of each element is quasi-independent of the others; thus, the aggregation is a simple approximate function. More investigations will be made in the future to detail dependencies and synergies among elements and consequently, the aggregation function will be accurate.

Data from EU product policies have the advantage of being based on homogeneous calculation methods and ratings for a particular product group. This is useful since the performance of components comes from an agreed evaluation process that makes it easier to compare products. However, the calculation methods applied in product policies might have some limitations in the accuracy of the performance figures they provide, and nowadays face the additional challenge of dealing with product systems. These figures are available either from the regulations themselves or from manufacturers’ technical documentation. In addition, as these product policies are continuously reviewed in order to adapt to market dynamics, performance calculation methods and thresholds are regularly updated. Designers, according to these product policies, can study the performance range (i.e. energy classes) of a component before choosing the product to be installed.

5.2. About the EU package concept

EC regulators have already recognised the limitations of considering isolated products instead of product systems, and have proposed to move their product policies from components to groups of products, giving data on performance at system level. This has been done, for example, through the aggregation of some products’ performance in ‘packages’, such as the packages of water heaters and solar device (EC 2013b, 2013c). This is a first attempt to benchmark HVAC systems through product policies. The energy benchmarking of systems engineering involves comparing the energy performance of a system against a common metric that represents the optimal performance of a reference system (Ke et al. 2013). Once the energy labels of packages are well established and documented (they came into force only in September 2015), comparisons among different systems will be possible and it is expected that this will lead to higher energy savings.

However, this package is not the whole system; it is a coherent set of components of the system, and is a candidate to be regarded as a single element (as sub-system) in the decomposition of the real system, with an associated level of performance. Implementing the package concept to the DHW case study and in accordance with Regulation EC 812/2013 (EC 2013b), the group of the boiler, the solar panel and the storage tank would deliver an energy efficiency package of 36%, with a package energy label C. The energy efficiency package is calculated by summing the performance of each (isolated) product. The energy flows from one component to the next are not addressed with a system approach, as in the method proposed in this paper. In addition to this package (boiler, solar panel and storage tank), distribution components and the taps of showers should be also included (as done in Section 3) since they are part of the system.

The design method proposed in Section 2 goes beyond the current EU package concept since it takes into account the specific global context and the component configuration, including every element (and their interactions), and is therefore more realistic in terms of the geographical conditions, building envelope and heating system.

5.3. Designers’ perspective

The simplified method proposed helps designers take informed decisions to better achieve energy-saving targets. In fact, the interesting point of the method for designers relies on the results of the improvement potential and combination of products’ performance levels, as demonstrated in the case study (Sections 4.2.1 and 4.2.2).

Two main results can be drawn from the case study. Firstly, the influence of the performance of individual components (Figure 5) on the system that can be studied using the proposed method. Results show that the performance of the storage tank has a much greater influence on the system than the performance of other components. A second result is that the proposed method helps designers study and compare various alternatives (system configurations), combining different product performance levels and simulating their system performance (Figure 6). It is then possible to reach a certain energy-saving target by combining components with different performance levels within the devices. This could be done either through simple modifications of the current devices or through substitution by a better device.

6. Conclusions and outlook

This paper proposes a simplified method to assess the design of efficient heating systems in residential buildings using data from EU product policies. The novelties of the method are that it allows the assessment of heating systems grounded on well-known and proven labelling schemes such as EU product policies (which are available at the early design stage and implemented by all manufacturers), and that it supports design activities at system level, enhancing informed decision-making on multiple design solutions based on different configurations of products currently available on the market.

The design method helps calculate the energy performance of a heating system according to the performance of its components. It facilitates the selection of the performance of each product that makes up the system and the combination of these to obtain an optimised solution at system level. The performance levels of products should be those of the EU product policies (the Ecodesign and Energy Labelling Directives, the EU GPP and the EU Ecolabel). When EU product policy data are not available, the method is flexible enough to allow designers to decide on which other calculation tool to use. As alternatives to EU product policy data, rules of thumb and professional software such as computational simulation tools can be used to assess product performance. The method can be used to enable the assessment of solutions, the comparison of alternatives and optimisation of the energy performance of the system at various stages of the design process, especially in the early stages. It also helps guide design activities towards energy-saving targets.

The method was applied to a real-life case study, and the fictive redesign of two heating systems (DHW and SH systems). Results show the relative influence of individual components currently available on the market in the overall system, and quantify the improvement potential. In the DHW system, upgrading the storage tank or the taps and showers to the maximum energy class could bring savings of 4162 and 985 kWh/y, respectively. An upgrade of the SH function of the boiler could lead to energy savings of 1012 kWh/y in the system. It also shows that the overall energy performance of a system can be optimised through different solutions using the right combination of product performance levels. To achieve system savings of at least 30% to 50%, one option would be to upgrade the storage tank to an F energy class and replace the taps and showers by ones that save 18% of the energy demand. Finally, the case study shows that energy performance indicators can be combined with other types of indicators such as costs to support informed design decisions.

Future work will deal with the robustness of the method and systematic sets of experiments on various HVAC systems to extract the main drivers of design for system optimisation. This could also include systematic analyses of synergies among system elements to help carry out the decomposition/aggregation process. Thus, even though the method has been developed and tested only on the design of heating systems, it could be extended to support all HVAC systems, and generalised to any other type of system for which regulations are available. The work presented in this paper could be also useful for formulating product policies thus helping better set up a product rating scheme for heating systems and other HVAC systems.

Notes on contributors

Maria Calero-Pastor has a degree in engineering with a specialisation on environment and natural resources by the Polytechnic University of Valencia (Spain). She has worked since 2007 in several research institutes on ecodesign and life cycle assessment in different sectors (packaging, construction products and appliances). The last four years, she worked in the Joint Research Center of the European Commission (Ispra, Italy), giving scientific and technical support to policy development, in particular on sustainable product policies. In addition, she has been doing her PhD thesis at Universite Grenoble Alpes on environmental assessment methods on product systems and how product policies can support design decisions.

Fabrice Mathieux is research staff at in the European Commission Joint Research Centre (DG JRC). He works on life cycle-based assessment methods and data applied to materials / products / systems, and on their use for decision making (e.g. concerning raw materials, resource efficiency, Ecodesign). He graduated in Mechanical engineering and holds a Master Degree in waste Management and LCA. His Industrial Engineering PhD Thesis concerned design for recycling of electr(on)ics. Before joining the EC, he led research in various universities in France and beyond (e.g. UK, Fiji islands).

Daniel BRISSAUD has been a professor of engineering design and eco-design at Universite Grenoble Alpes since 1998. He is a member of the Institute of Engineering (Grenoble INP) and trains French engineers in products, services and systems design. He has been a CIRP fellow, the head of the research cluster on engineering for industry and innovation at Rhone-Alpes area since 2007 and has led the French survey on sustainable production systems for the future in 2013. His scientific interests are Eco-design, environmental assessment, lifecyle engineering, clean technologies, product-service systems design and integrated design. He authored more than 70 papers in international journals and books and mentored more than 20 PhD theses.

Dewulf performs research in the areas of environmental chemistry, environmental technology and clean technology at Ghent University. Key in his work is managing natural resources in a technically efficient way, performing thermodynamics-based sustainability analysis at process, plant and cradle-to-gate level to support the development and assessment of new technologies.

Disclosure statement

No potential conflict of interest was reported by the authors.

Acknowledgements

The authors thank Gianni Frison for providing the data of the case study. We also acknowledge Gráinne Mulhern for the language proof reading of the article.