No 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.

No 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.