Performance analysis and comparison of various techniques for short-term load forecasting

• Published in: Energy reports Vol. 9; pp. 799 - 808
• Main Authors: Shahare, Kamini, Mitra, Arghya, Naware, Dipanshu, Keshri, Ritesh, Suryawanshi, H.M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-03-2023; Elsevier
• Subjects: CNN-LSTM hybrid model; IEEE dataport; Machine learning; Short term load forecasting; CNN-LSTM hybrid model; IEEE dataport; Short term load forecasting; Machine learning
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2022.11.086

Rapidly varying load demand is one of the greatest problems that distribution system operators are now experiencing. Many researchers have been implemented the load demand requirement using traditional methods and machine learning methods. Both the methods have their own pros and cons according to dataset available for a particular site. This paper aims to predict load demand using different stochastic and deterministic approaches for short term load forecasting (STLF). Utilizing a variety of dependent characteristics, historical load data for two years is collected from IEEE dataport. The methods used in this study are exponential smoothing of traditional method and machine learning methods such as support vector machine (SVM), ensemble, artificial neural network (ANN), convolution neural network (CNN), long-short term memory (LSTM) and CNN-LSTM hybrid Model. All of these methods are compared and the best option is chosen of coefficient of correlation (R). The best method is found to be CNN-LSTM hybrid with R of 95.05%. The algorithm is implemented in PYTHON platform which is more compatible for machine learning applications.