Combinatorial Component Day-Ahead Load Forecasting through Unanchored Time Series Chain Evaluation

Dimitrios Kontogiannis (), Dimitrios Bargiotas (), Athanasios Fevgas, Aspassia Daskalopulu and Lefteri H. Tsoukalas
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Dimitrios Kontogiannis: Department of Electrical and Computer Engineering, School of Engineering, University of Thessaly, 38334 Volos, Greece
Dimitrios Bargiotas: Department of Electrical and Computer Engineering, School of Engineering, University of Thessaly, 38334 Volos, Greece
Athanasios Fevgas: Department of Electrical and Computer Engineering, School of Engineering, University of Thessaly, 38334 Volos, Greece
Aspassia Daskalopulu: Department of Electrical and Computer Engineering, School of 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

Energies, 2024, vol. 17, issue 12, 1-46

Abstract: Accurate and interpretable short-term load forecasting tasks are essential to the optimal operation of liberalized electricity markets since they contribute to the efficient development of energy trading and demand response strategies as well as the successful integration of renewable energy sources. Consequently, performant day-ahead consumption forecasting models need to capture feature nonlinearities, analyze system dynamics and conserve evolving temporal patterns in order to minimize the impact of noise and adapt to concept drift. Prominent estimators and standalone decomposition-based approaches may not fully address those challenges as they often yield small error rate improvements and omit optimal time series evolution. Therefore, in this work we propose a combinatorial component decomposition method focused on the selection of important renewable generation component sequences extracted from the combined output of seasonal-trend decomposition using locally estimated scatterplot smoothing, singular spectrum analysis and empirical mode decomposition methods. The proposed method was applied on five well-known kernel models in order to evaluate day-ahead consumption forecasts on linear, tree-based and neural network structures. Moreover, for the assessment of pattern conservation, an intuitive metric function, labeled as Weighted Average Unanchored Chain Divergence (WAUCD), based on distance scores and unanchored time series chains is introduced. The results indicated that the application of the combinatorial component method improved the accuracy and the pattern conservation capabilities of most models substantially. In this examination, the long short-term memory (LSTM) and deep neural network (DNN) kernels reduced their mean absolute percentage error by 46.87% and 42.76% respectively and predicted sequences that consistently evolved over 30% closer to the original target in terms of daily and weekly patterns.

Keywords: load forecasting; electricity demand prediction; load curve analysis; renewable energy integration; forecasting performance evaluation; machine learning; time series analysis; preprocessing; data mining; time series chains (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: 2024
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