Feature Selection Technique Using Multiple Linear Regression for Accurate Electricity Demand Forecasting

Abstract

The rising power demand, driven by population growth, technological innovations, and the advent of smart cities, necessitates precise forecasting to ensure efficient energy distribution and align supply with demand. This paper presents a novel methodology for predicting short-term power consumption through machine learning approaches, specifically employing multiple linear regression for feature selection. In this study, two models are implemented and compared: Support Vector Regression (SVR) and Long-Short-Term Memory (LSTM). Exploratory data analysis was used to discover the relationships and associations between variables. It reveals that temperature, humidity, time of day, and season are major determinants of electricity use. The results indicate that the LSTM model surpasses Support Vector Regression (SVR) in terms of accuracy and precision. By incorporating multiple linear regression (MLR) for feature selection, the performance of both models improved, with precision gains of 29.1% for SVR and 18.19% for LSTM. Removing extraneous elements, such as wind speed and diffuse solar radiation, enhanced the models’ efficiency and interpretability, allowing for a focus on the most significant factors. The study’s findings underscore the need to optimize feature selection to enhance forecast accuracy and streamline models. This method provides critical insights for enhancing energy management strategies and facilitating sustainable power distribution in light of rising global energy demand.

Keywords

Electricity load forecasting / Feature selection, Machine learning / Multiple linear regression / Long Short-Term Memory

Acknowledgments

We would like to express our sincere gratitude to all those who contributed to the successful completion of this research.

Author Contributions

Conceptualization, G.N., M.N. and O.A.-K.; Methodology, G.N.; Software, G.N. and A.R.; Validation, A.H., G.N., and O.A.-K.; Formal Analysis, O.K. and A.R.; Investigation, G.N. and A. R.; Resources, M.N.; Data Curation, G.N.; Writing—Original Draft Preparation, G.N., and M.N.; Writing—Review & Editing, O.A.-K. and A.R.; Visualization, O.K. and A. R.; Supervision, A.H.; Project Administration, M.N.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The dataset used in this study is publicly available and can be accessed freely online.

Funding

This research received no external funding.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] Ahmad T, Chen H, Guo Y, Wang J. A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review. Energy Build. 2018, 165, 301-320.

[2] Nasserddine G, Nassereddine M, El Arid AA. Internet of things integration in renewable energy systems In Handbook of research on Applications of AI, Digital Twin, and Internet of Things for Sustainable Development; IGI Global: Hershey, PA, USA, 2023; pp. 159-185.

[3] Ghalehkhondabi I, Ardjmand E, Weckman GR, Young WA. An overview of energy demand forecasting methods published in 2005-2015. Energy Syst. 2017, 8, 411-447.

[4] Debnath KB, Mourshed M. Renew. Sustain. Forecasting methods in energy planning models. Energy Rev. 2018, 88, 297-325.

[5] El Samad M, Nasserddine G, Kheir A. Introduction to Artificial Intelligence in Artificial Intelligence and Knowledge Processing; CRC Press: Boca Raton, FL, USA, 2023; pp. 1-14.

[6] Deb C, Zhang F, Yang J, Lee SE, Shah KW. A review on time series forecasting techniques for building energy consumption. Renew. Sustain. Energy Rev. 2017, 74, 902-924.

[7] Shih S, Sun F, Lee H. Temporal pattern attention for multivariate time series forecasting. Mach. Learn. 2019, 108, 1421-1441.

[8] Yaslan Y, Bican B. Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting. Measurement 2017, 103, 52-61.

[9] Noureen S, Atique S, Roy V, Bayne S. Analysis and application of seasonal ARIMA model in energy demand forecasting: A case study of small scale agricultural load. In Proceedings of the 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), Dallas, TX, USA, 4-7 August 2019.

[10] Román-Portabales A, López-Nores M, Pazos-Arias JJ. Systematic review of electricity demand forecast using ANN-based machine learning algorithms. Sensors 2021, 21, 4544.

[11] Tarmanini C, Sarma N, Gezegin C, Ozgonenel O. Short term load forecasting based on ARIMA and ANN approaches. Energy Rep. 2023, 9, 550-557.

[12] Unutmaz YE, Demirci A, Tercan SM, Yumurtaci R. Electrical energy demand forecasting using artificial neural network. In Proceedings of the 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey, 11-13 June 2021.

[13] Bedi J, Toshniwal D. Deep learning framework to forecast electricity demand. Appl. Energy 2019, 238, 1312-1326.

[14] del Real AJ, Dorado F, Durán J. Energy demand forecasting using deep learning: applications for the French grid. Energies 2020, 13, 2242.

[15] Marino DL, Amarasinghe K, Manic M.Building energy load forecasting using deep neural networks. In Proceedings of the IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, 23-26 October 2016.

[16] Ghazal TM. Energy demand forecasting using fused machine learning approaches. Intell. Autom. Soft. Comput. 2022, 31, 539-553.

[17] Li C, Ding Z, Zhao D, Yi J, Zhang G. Building energy consumption prediction: An extreme deep learning approach. Energies 2017, 10, 1525.

[18] Pallonetto F, Jin C, Mangina E. Forecast electricity demand in commercial building with machine learning models to enable demand response programs. Energy AI 2022, 7, 100121.

[19] Komorowski M, Marshall DC, Salciccioli JD, Crutain Y.Exploratory data analysis. In Secondary Analysis of Electronic Health Records; Springer: Berlin/Heidelberg, Germany, 2016.

[20] Mirkin B. Core Data Analysis: Summarization, Correlation, and Visualization; Springer International Publishing: Cham, Switzerland, 2019.

[21] Pandis N. Linear regression. Am. J. Orthod. Dentofac. Orthop. 2016, 149, 431-434.

[22] Darlington RB, Hayes AF. Regression Analysis and Linear Models: Concepts, Applications, and Implementation; Guilford Publications: New York, NY, USA, 2016.

[23] Andrade C. The P value and statistical significance: misunderstandings, explanations, challenges, and alternatives. Indian J. Psychol. Med. 2019, 41, 210-215.

[24] Hasan MA, Hasan MK, Mottalib MA. Linear regression-based feature selection for microarray data classification. Int. J. Data Min. Bioinform. 2015, 11, 167-179.

[25] Nasserddine G, Amal A. Decision-making systems In Encyclopedia of Data Science and Machine Learning; IGI Global Scientific Publishing: Hershey, PA, USA, 2023; pp. 1391-1407.

[26] García S, Luengo J, Herrera F. Data Preprocessing in Data Mining; Springer International Publishing: Cham, Switzerland, 2015.

[27] Guo Y, Wang W, Wang X. A robust linear regression feature selection method for data sets with unknown noise. IEEE Trans. Knowl. Data Eng. 2021, 35, 31-44.

[28] Nassreddine G, El Arid A, Nassereddine M, Al-Khatib O, Arram A, El Abed A. Enhancing the Efficacy of Short-Term Prediction Models for Solar Photovoltaic Systems: An Influence Examination of Chronological and Meteorological Factors. IEEE Access 2025, 13, 66787-66808.

[29] Yu Y, Si X, Hu C, Zhang J. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 2019, 31, 1235-1270.

[30] Hrnjica B, Mehr AD. Energy demand forecasting using deep learning In Smart Cities Performability, Cognition, & Security; Springer: Berlin, Germany, 2019; pp. 71-104.

[31] Eseye AT, Lehtonen M, Tukia T, Uimonen S, Millar RJ. Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems. IEEE Access 2019, 7, 91463-91475.

[32] Fedesoriano. Electric Power Consumption. Available online: accessed on 2 June 2024).