A Novel Hybrid Model Based on an Optimized Multilayer Perceptron Neural Network and the Cuckoo Search Algorithm for Forecasting Monthly Household Electricity Consumption: A Case Study of Ilam County

Sadegh Feizollahi; Roham Mokhtari Dezaki; Ahmadreza Fatahi Nezhad; Mohammad Heidari Goujani

Authors

Sadegh Feizollahi Department of management, Il.C., Islamic Azad university, Ilam, Iran https://orcid.org/0000-0002-7505-8536
Roham Mokhtari Dezaki Department of management, ShK.C., Islamic Azad university, Shahr-e Kord, Iran Author https://orcid.org/0009-0006-9565-9538
Ahmadreza Fatahi Nezhad Department of management, ShK.C., Islamic Azad university, Shahr-e Kord, Iran Author https://orcid.org/0009-0008-0405-8222
Mohammad Heidari Goujani Department of management, ShK.C., Islamic Azad university, Shahr-e Kord, Iran Author https://orcid.org/0000-0002-8084-7265

Keywords:

multilayer perceptron network, electricity consumption forecasting, machine learning, electricity consumption in Ilam

Abstract

The objective of this study is to propose a novel method for forecasting the monthly electricity consumption of subscribers in Ilam County. The results of this study can assist energy-supply companies in obtaining an overall understanding of household electricity consumption patterns. For forecasting purposes, a combination of a multilayer perceptron (MLP) neural network and the Cuckoo Search Optimization Algorithm is employed. In the proposed method, the estimation process is performed in three stages: “preprocessing,” “feature selection,” and “estimation.” The minimum redundancy–maximum relevance (MRMR) algorithm is used for feature selection. This algorithm identifies the optimal features for forecasting electricity consumption such that they exhibit minimum redundancy and maximum relevance to the target variable. In the estimation stage, a combination of three multilayer perceptron neural networks is employed, and their configurations are adjusted using the Cuckoo Search Optimization Algorithm so as to minimize the estimation error. The results of the performance evaluation of the proposed method indicated that the accuracy of forecasting subscribers’ monthly electricity consumption can be maximized using six features. Furthermore, comparison of the proposed method with other existing techniques showed that the proposed hybrid system can perform the forecasting process with a mean absolute error of 5.66 kWh, representing an error reduction of at least 29.4% compared with previous methods.

References

Ardabili, S., Mosavi, A., & Várkonyi-Kóczy, A. R. (2019). Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities. In (pp. 191-201). Springer.

Chen, Y. (2025). Research on Home Electricity Prediction Based on LSTM. Applied and Computational Engineering. https://doi.org/10.54254/2755-2721/2025.tj23583

Gorjian, S., Zadeh, B. N., Eltrop, L., Shamshiri, R. R., & Amanlou, Y. (2019). Solar Photovoltaic Power Generation in Iran: Development, Policies, and Barriers. Renewable and Sustainable Energy Reviews, 106, 110-123.

Hosseini, B. (2025). Forecasting Household Monthly Electricity Consumption Using the Similar Pattern Algorithm. Academia Green Energy. https://doi.org/10.20935/acadenergy7500

Kaboli, S. H. A., Selvaraj, J., & Rahim, N. A. (2016). Long-Term Electric Energy Consumption Forecasting via Artificial Cooperative Search Algorithm. Energy, 115, 857-871.

Lotfalipour, M. R., Falahi, M. A., & Ashena, M. (2010). Economic Growth, CO2 Emissions, and Fossil Fuels Consumption in Iran. Energy, 35(12), 5115-5120.

Mareli, M., & Twala, B. (2018). An Adaptive Cuckoo Search Algorithm for Optimization. Applied Computing and Informatics, 14(2), 107-115.

Mosavi, A., & Bahmani, A. (2019). Energy Consumption Prediction Using Machine Learning: A Review. Preprints.

Rahman, A., Srikumar, V., & Smith, A. D. (2018). Predicting Electricity Consumption for Commercial and Residential Buildings Using Deep Recurrent Neural Networks. Applied Energy, 212, 372-385.

Zhafran, F., & Miefthawati, N. P. (2024). Forecasting Electricity Consumption and Its Relationship with Climate Change in Pekanbaru City. Protek: Jurnal Ilmiah Teknik Elektro. https://doi.org/10.33387/protk.v11i3.7309

Zhao, Z., Anand, R., & Wang, M. (2019). Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform.