Enhanced Short-Term Load Forecasting Using Artificial Neural Networks

Athanasios Ioannis Arvanitidis, Dimitrios Bargiotas, Aspassia Daskalopulu, Vasileios M. Laitsos and Lefteri H. Tsoukalas
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Athanasios Ioannis Arvanitidis: 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
Vasileios M. Laitsos: Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece
Lefteri H. Tsoukalas: School of Nuclear Engineering, Purdue University, West Lafayette, IN 47907, USA

Energies, 2021, vol. 14, issue 22, 1-14

Abstract: The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to ensure maximal use of current circuits, system flexibility and efficiency, as well as the relatively easy integration of renewable energy resources at all voltage levels. The current revolution in communication technologies and the Internet of Things (IoT) offers us an opportunity to supervise and regulate the power grid, in order to achieve more reliable, efficient, and cost-effective services. One of the most critical aspects of efficient power system operation is the ability to predict energy load requirements, i.e., load forecasting. Load forecasting is essential for balancing demand and supply and for determining electricity prices. Typically, load forecasting has been supported through the use of Artificial Neural Networks (ANNs), which, once trained on a set of data, can predict future loads. The accuracy of the ANNs’ prediction depends on the quality and availability of the training data. In this paper, we propose novel data pre-processing strategies, which we apply to the data used to train an ANN, and subsequently evaluate the quality of the predictions it produces, to demonstrate the benefits gained. The proposed strategies and the obtained results are illustrated using consumption data from the Greek interconnected power system.

Keywords: smart grids; load forecasting; artificial neural networks; data pre-processing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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