Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Enhanced Automated Deep Learning Application for Short Term Load Forecasting

Version 1 : Received: 12 May 2023 / Approved: 15 May 2023 / Online: 15 May 2023 (04:39:19 CEST)

A peer-reviewed article of this Preprint also exists.

Laitsos, V.; Vontzos, G.; Bargiotas, D.; Daskalopulu, A.; Tsoukalas, L.H. Enhanced Automated Deep Learning Application for Short-Term Load Forecasting. Mathematics 2023, 11, 2912. Laitsos, V.; Vontzos, G.; Bargiotas, D.; Daskalopulu, A.; Tsoukalas, L.H. Enhanced Automated Deep Learning Application for Short-Term Load Forecasting. Mathematics 2023, 11, 2912.

Abstract

Nowadays, power sector is an area that gather great scientific interest, due to events such as the increase in electricity prices in the wholesale energy market and new investments due to technological development in various sectors. These new challenges have in turn created new needs, such as the accurate prediction of the electrical load of the end users. On the occasion of the new challenges, Artificial Neural Networks approaches have become increasingly popular due to their ability to adopt efficiently to time-series predictions. In this paper, it is presented the development of a model which, through an automated process, will provide an accurate prediction of electrical load for the island of Thira in Greece. Through an automated application, deep learning load forecasting models have been created, such as Multilayer Perceptron, Long Short-Term Memory (LSTM), Convolutional Neural Network One Dimensional (CNN-1D), CNN-LSTM, Temporal Convolutional Network (TCN) and a proposed hybrid model called Convolutional LSTM Encoder-Decoder. The results in terms of prediction accuracy show satisfactory performances for all models, with the proposed hybrid model achieving the best accuracy.

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

Subject

Engineering, Electrical and Electronic Engineering

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