Machine learning algorithms for predicting electrical load demand: an evaluation and comparison

• Published in: Sadhana (Bangalore) Vol. 49; no. 1; p. 40
• Main Authors: Goswami, Kakoli, Kandali, Aditya Bihar
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 25.01.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Datasets; Deep learning; Distance learning; Electric power demand; Electric power systems; Electrical loads; Electricity; Energy consumption; Engineering; Forecasting; Forecasting techniques; Literature reviews; Machine learning; Neural networks; Peak demand; Peak load; Prediction models; Programming languages; Python; Real time; Root-mean-square errors; Statistical methods; Time series; time series analysis; computational intelligence; machine learning; supervised learning; Electrical load forecasting; deep neural network
• ISSN: 0973-7677; 0256-2499; 0973-7677
• DOI: 10.1007/s12046-023-02354-2

Forecasting of load is essential for operating power systems. India recently witnessed one of the worst power crisis with the highest ever power demand of 207 GW on April 29, 2022. The demand in the month of May and June 2022 was estimated to reach 215 GW. The peak demand this year 2023, according to the electricity ministry, is predicted to be around 230 GW from April to June. The inability to meet certain fundamental issues as power can take a toll on any country’s economy. Proper prediction helps in proper decision making and planning. The main objective of this paper is to predict day ahead electrical load demand for Assam. Statistical and Machine Learning Algorithms has been studied. The study has been carried out using real-time data for the years 2016, 2017 and 2018. The paper presents a detailed analysis of the different hyper parameters of the deep learning models and their effect is seen on the learning efficiency. A novel stacked forecasting model is proposed using neural networks as base learners and CatBoost as the meta-learner. The performance of the proposed model has been evaluated and compared with individual models in terms of training time and accuracy using different error metrics namely MAE, MSE, RMSE, MAPE and R 2 score. A comparison of the proposed prediction model with the prediction models available in literature has been presented. The conclusion states that both the statistical and machine learning algorithms used in this study act as useful tools for daily load forecasting with considerable accuracy; yet machine learning algorithm outperforms the statistical methods. The entire work has been done in Google Colaboratory using Python as the programming language.