Enhanced Automated Deep Learning Application for Short-Term Load Forecasting

Vasileios Laitsos, Georgios Vontzos, Dimitrios Bargiotas (), Aspassia Daskalopulu and Lefteri H. Tsoukalas
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Vasileios Laitsos: Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece
Georgios Vontzos: Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece
Dimitrios Bargiotas: Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece
Aspassia Daskalopulu: Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece
Lefteri H. Tsoukalas: Center for Intelligent Energy Systems (CiENS), School of Nuclear Engineering, Purdue University, West Lafayette, IN 47906, USA

Mathematics, 2023, vol. 11, issue 13, 1-21

Abstract: In recent times, the power sector has become a focal point of extensive scientific interest, driven by a convergence of factors, such as mounting global concerns surrounding climate change, the persistent increase in electricity prices within the wholesale energy market, and the surge in investments catalyzed by technological advancements across diverse sectors. These evolving challenges have necessitated the emergence of new imperatives aimed at effectively managing energy resources, ensuring grid stability, bolstering reliability, and making informed decisions. One area that has garnered particular attention is the accurate prediction of end-user electricity load, which has emerged as a critical facet in the pursuit of efficient energy management. To tackle this challenge, machine and deep learning models have emerged as popular and promising approaches, owing to their having remarkable effectiveness in handling complex time series data. In this paper, the development of an algorithmic model that leverages an automated process to provide highly accurate predictions of electricity load, specifically tailored for the island of Thira in Greece, is introduced. Through the implementation of an automated application, an array of deep learning forecasting models were meticulously crafted, encompassing the Multilayer Perceptron, Long Short-Term Memory (LSTM), One Dimensional Convolutional Neural Network (CNN-1D), hybrid CNN–LSTM, Temporal Convolutional Network (TCN), and an innovative hybrid model called the Convolutional LSTM Encoder–Decoder. Through evaluation of prediction accuracy, satisfactory performance across all the models considered was observed, with the proposed hybrid model showcasing the highest level of accuracy. These findings underscore the profound significance of employing deep learning techniques for precise forecasting of electricity demand, thereby offering valuable insights with which to tackle the multifaceted challenges encountered within the power sector. By adopting advanced forecasting methodologies, the electricity sector moves towards greater efficiency, resilience and sustainability.

Keywords: load forecasting; long short-term memory; Temporal Convolution Networks; Multilayer Perceptron; Convolutional Neural Networks; CNN–LSTM; Convolutional LSTM Encoder–Decoder; evaluation metrics; power sector; data analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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