Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals

Sumit Saroha, Marta Zurek-Mortka, Jerzy Ryszard Szymanski, Vineet Shekher and Pardeep Singla
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Sumit Saroha: Department of Electrical Engineering, Guru Jambheshwar University of Science and Technology, Hisar 125001, India
Marta Zurek-Mortka: Department of Control Systems, Łukasiewicz Research Network—Institute for Sustainable Technologies, 26600 Radom, Poland
Jerzy Ryszard Szymanski: Faculty of Transport, Electrical Engineering and Computer Science, Kazimierz Pulaski University of Technology and Humanities, 26000 Radom, Poland
Vineet Shekher: Department of Electrical and Electronics Engineering, Birsa Institute of Technology Sindri, Dhanbad 828123, India
Pardeep Singla: Department of Electronics and Communications Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Sonepat 131001, India

Energies, 2021, vol. 14, issue 19, 1-21

Abstract: In order to analyze the nature of electrical demand series in deregulated electricity markets, various forecasting tools have been used. All these forecasting models have been developed to improve the accuracy of the reliability of the model. Therefore, a Wavelet Packet Decomposition (WPD) was implemented to decompose the demand series into subseries. Each subseries has been forecasted individually with the help of the features of that series, and features were chosen on the basis of mutual correlation among all-time lags using an Auto Correlation Function (ACF). Thus, in this context, a new hybrid WPD-based Linear Neural Network with Tapped Delay (LNNTD) model, with a cyclic one-month moving window for a one-year market clearing volume (MCV) forecasting has been proposed. The proposed model has been effectively implemented in two years (2015–2016) and unconstrained MCV data collected from the Indian Energy Exchange (IEX) for 12 grid regions of India. The results presented by the proposed models are better in terms of accuracy, with a yearly average MAPE of 0.201%, MAE of 9.056 MWh, and coefficient of regression ( R 2 ) of 0.9996. Further, forecasts of the proposed model have been validated using tracking signals (TS’s) in which the values of TS’s lie within a balanced limit between ?492 to 6.83, and universality of the model has been carried out effectively using multiple steps-ahead forecasting up to the sixth step. It has been found out that hybrid models are powerful forecasting tools for demand forecasting.

Keywords: forecasting; market clearing volume; neural network; tracking signals; wavelet packets (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 (1)

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