Estimating electricity consumption at city-level through advanced machine learning methods

Figures & data

Table 1. Synthetized related work.

Article	Method	Purpose	Dataset length	Sampling interval
Feilmeier (2015)	Multi-Layer Perceptron	Forecasting electricity production and consumption in a household with solar panels	5 months	5 min
Gellert et al. (2022)	TBATS	Forecasting electricity production and consumption in a household with solar panels	5 months	5 min
Bachici & Gellert (2020)	Long Short-Term Memory	Forecasting electricity production and consumption in a household with solar panels	5 months	5 min
Gellert et al. (2019)	Markov model	Forecasting electricity production and consumption in a household with solar panels	5 months	5 min
Escrivá-Escrivá et al. (2011)	Multi-Layer Perceptron	Short-term prediction of total power consumption in buildings	450 days	15 min
Zhang et al. (2020)	LSTM-ANN hybrid	Short-term prediction of building-level energy consumption	5 months	1 h
Kim & Cho (2019)	CNN-LSTM hybrid model	Prediction of residential electricity demand	4 years	1 min
Le et al. (2019)	CNN-BiLSTM hybrid	Energy consumption prediction at household-level	4 years	1 min
Cai et al. (2019)	Gate CNNs	Day-ahead building-level load forecasting	1 year	1 h
Bedi et al. (2020)	Elman neural network	Prediction of electricity use in IoT-driven buildings	8 weeks	10 min
Fumo & Biswas (2015)	Linear regression and quadratic Regression models	Prediction of residential energy consumption	1 month	5 min
Li et al. (2021)	Swarm intelligence-based ensemble model	Short-term electricity consumption prediction for buildings	6 months	1 h
Zhou et al. (2022)	Latent stochastic recurrent neural network	Prediction of electricity generation demand for large-scale hydropower stations	2 years	1 h
Rosas et al. (2022)	Hybrid CNN-LSTM	Prediction of regenerable energy production and consumption in a public building	1 year	10 min
Wang et al. (2021)	Gradient Boosting Tree model	Prediction of daily electricity consumption at city scale	4 years	1 h
Lee et al. (2022)	Fuzzy time series model	Prediction of electricity consumption at country-level	10 years	1 month
Reddy et al. (2023)	KNN, XGBoost, Random Forest, LSTM	Electricity usage prediction from the power utility provider	1 year	1 h
Stingl et al. (2018)	Linear regression models	Forecasting annual electricity consumption at industrial level	5 years	1 d
Porteiro et al. (2022)	Linear regression models	Day-ahead electricity demand forecasting at industry / city / country level	4 / 2 / 9 years	1 h
Lazzari et al. (2022)	XGBoost-ANN hybrid	Day-ahead electricity consumption forecasting for residential households	1 year	1 h
Ghimire et al. (2023)	ICMD-ANN-EDLSTM hybrid	daily electricity demand prediction at state level	10 years	30 min

Figure 1. Methodology flowchart

Figure 2. The Structure of the fuzzy controller.

Figure 3. Numerical domain divided into fuzzy regions.

Figure 4. The structure of a fuzzy rule base.

Figure 5. Input data mapping onto fuzzy rules.

Figure 6. The result of the interference.

Table 2. Excerpt of electricity consumption from the AFEE dataset.

cc [MWh]	cpt [MWh]	cc_cpt [MWh]
107.971	126.260	234.231
82.731	120.800	203.531

Figure 7. Data transformation.

Figure 8. The influence of the data history length over the mean absolute error.

Figure 9. Varying the seasonality.

Figure 10. The influence of the number of inputs for the fuzzy rules.

Figure 11. The influence of the number of training values.

Figure 12. The influence of the method of solving the fuzzy rule base conflicts.

Figure 13. The influence of the number of counter states.

Figure 14. The influence of the threshold error value.

Figure 15. The influence of the defuzzification method.

Figure 16. Comparison of the mean absolute error between different predictors.

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Data availability statement

The code and excerpts from the dataset are available upon request.