A novel method on the optimization problem of energy conservation in public buildings

ABSTRACT

In recent years, the problem of energy consumption has received considerable attention. Among them, the proportion of energy consumption in public buildings accounts for a substantial part, and there is a large space for energy saving in this field. Practically, the major limiting factor for energy consumption optimization is indoor environment comfort degree. As such, a trade-off problem between energy conservation and indoor environment must be resolved. In this paper, an office in a public building is chosen as the experimental object, with 2520 real observations collected for two months, a series of intelligence based techniques are combined and applied to deal with this optimization problem. First, Partial Rank Correlation Coefficient (PRCC) was employed to perform correlation analysis for a set of candidate variables, such that the computational time of subsequent model training is reduced by 8.32 seconds as compared to the case where no PRCC is in use. Following this, Genetic Algorithm (GA)-based Levenberg-Marquardt algorithm (LM) are applied collectively to optimize the conventional Back Propagation (BP) neural network, and results suggest that there was a marked improvement in neural model performance, on one hand the epoch number for convergence drops from 72 to 13, on the other hand the mean squared error for test set is reduced by 7.46e-4 (from 9.93e-4 to 2.47e-4). Finally, pigeon-inspired Optimization (PIO) algorithm is employed to address the formulated optimization problem and the optimal value for Predicted Mean Vote (PMV) and Indoor Air Quality (IAQ) are −0.4775 and 0.007826, respectively. To verify the validity of our results that derived by the proposed approach, corresponding parameters are applied in a practical scenario. The experimental result indicates that the energy efficiency is improved by around 18.6% compared to that yielded before optimization is performed, which demonstrates the applicability and effectiveness of the proposed method.

KEYWORDS:

• Energy optimization
• indoor comfortability
• evolutionary algorithm
• network optimization
• correlation analysis

Nomenclature

$\mathit{N}$The total number of indoor gas pollutants of interest

$\mathit{Hi}$ Highest concentration of the contaminant

$\mathit{Lo}$ Lowest concentration of the contaminant

$C_p$ Mass concentration value of pollutant index $\mathit{P}$

$\mathit{B}{P}_{\mathit{Hi}}$ High value of contaminant concentration limit close to $C_p$

$\mathit{B}{P}_{\mathit{Lo}}$ Low value of contaminant concentration limit close to $C_p$

$\mathit{IIAQ}{I}_{\mathit{Hi}}$ The indoor air quality index corresponding to $\mathit{B}{P}_{\mathit{Hi}}$

$\mathit{IIAQ}{I}_{\mathit{Lo}}$ The indoor air quality index corresponding to $\mathit{B}{P}_{\mathit{Lo}}$

$T_{\mathit{cl}}$ Clothing surface temperature (${ }^o\mathit{C}$); $T_z$ Air temperature(${ }^o\mathit{C}$)

$p_a$ Water vapor partial pressure ($p_a$); $h_c$ Convective heat transfer coefficient ($\mathit{W}/m^2\mathit{K}$)

$f_{\mathit{cl}}$ Clothing surface area factor; $\mathit{M}$ Metabolic rate ($\mathit{W}/m^2$)

$\mathit{W}$ excreted work ($/m^2$); $I_{\mathit{cl}}$ Clothing index; $v_a$ Air velocity (m/s)

$T_{\mathit{rm}}$ Mean radiant temperature (${ }^o\mathit{C}$); $\mathit{RH}$ Relative humidity

$w_Z$ Humidity of the air; $w_{\mathit{sat}}$ Saturation humidity of the air

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [61873006]; Beijing Natural Science Foundation [4204087]