Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting

Athanasios Ioannis Arvanitidis, Dimitrios Bargiotas, Aspassia Daskalopulu, Dimitrios Kontogiannis, Ioannis P. Panapakidis 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
Dimitrios Kontogiannis: Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece
Ioannis P. Panapakidis: 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 47907, USA

Energies, 2022, vol. 15, issue 4, 1-14

Abstract: The stable and efficient operation of power systems requires them to be optimized, which, given the growing availability of load data, relies on load forecasting methods. Fast and highly accurate Short-Term Load Forecasting (STLF) is critical for the daily operation of power plants, and state-of-the-art approaches for it involve hybrid models that deploy regressive deep learning algorithms, such as neural networks, in conjunction with clustering techniques for the pre-processing of load data before they are fed to the neural network. This paper develops and evaluates four robust STLF models based on Multi-Layer Perceptrons (MLPs) coupled with the K-Means and Fuzzy C-Means clustering algorithms. The first set of two models cluster the data before feeding it to the MLPs, and are directly comparable to similar existing approaches, yielding, however, better forecasting accuracy. They also serve as a common reference point for the evaluation of the second set of two models, which further enhance the input to the MLP by informing it explicitly with clustering information, which is a novel feature. All four models are designed, tested and evaluated using data from the Greek power system, although their development is generic and they could, in principle, be applied to any power system. The results obtained by the four models are compared to those of other STLF methods, using objective metrics, and the accuracy obtained, as well as convergence time, is in most cases improved.

Keywords: short-term load forecasting; multi-layer perceptrons; K-Means; Fuzzy C-Means (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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