Short-Term Load Forecasting Based on Optimized Random Forest and Optimal Feature Selection

Bianca Magalhães, Pedro Bento, José Pombo, Maria do Rosário Calado and Sílvio Mariano ()
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Bianca Magalhães: IT—Instituto de Telecomunicações, University of Beira Interior, 6201-001 Covilhã, Portugal
Pedro Bento: IT—Instituto de Telecomunicações, University of Beira Interior, 6201-001 Covilhã, Portugal
José Pombo: IT—Instituto de Telecomunicações, University of Beira Interior, 6201-001 Covilhã, Portugal
Maria do Rosário Calado: IT—Instituto de Telecomunicações, University of Beira Interior, 6201-001 Covilhã, Portugal
Sílvio Mariano: IT—Instituto de Telecomunicações, University of Beira Interior, 6201-001 Covilhã, Portugal

Energies, 2024, vol. 17, issue 8, 1-21

Abstract: Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations.

Keywords: short-term load forecasting; random forest; regression tree; input patterns (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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