Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland—Swietokrzyskie Voivodeship
Author:
Pikus Michal1ORCID, Wąs Jarosław1ORCID
Affiliation:
1. AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Applied Computer Science, av. Mickiewicza 30, 30-059 Krakow, Poland
Abstract
Forecasting electricity demand is of utmost importance for ensuring the stability of the entire energy sector. However, predicting the future electricity demand and its value poses a formidable challenge due to the intricate nature of the processes influenced by renewable energy sources. Within this piece, we have meticulously explored the efficacy of fundamental deep learning models designed for electricity forecasting. Among the deep learning models, we have innovatively crafted recursive neural networks (RNNs) predominantly based on LSTM and combined architectures. The dataset employed was procured from a SolarEdge designer. The dataset encompasses daily records spanning the past year, encompassing an exhaustive collection of parameters extracted from solar farm (based on location in Central Europe (Poland Swietokrzyskie Voivodeship)). The experimental findings unequivocally demonstrated the exceptional superiority of the LSTM models over other counterparts concerning forecasting accuracy. Consequently, we compared multilayer DNN architectures with results provided by the simulator. The measurable results of both DNN models are multi-layer LSTM-only accuracy based on R2—0.885 and EncoderDecoderLSTM R2—0.812.
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Reference10 articles.
1. Efficiency analysis of photovoltaic systems installed in different geographical locations;Demirkiran;Open Chem.,2022 2. Bochenek, B., Jurasz, J., Jaczewski, A., Stachura, G., Sekuła, P., Strzyżewski, T., Wdowikowski, M., and Figurski, M. (2021). Day-Ahead Wind Power Forecasting in Poland Based on Numerical Weather Prediction. Energies, 14. 3. Gołȩbiewski, D., Barszcz, T., Skrodzka, W., Wojnicki, I., and Bielecki, A. (2022). A New Approach to Risk Management in the Power Industry Based on Systems Theory. Energies, 15. 4. Roberts, J., Cassula, A., Freire, J., and Prado, P. (2015, January 8–11). Simulation and Validation Of Photovoltaic System Performance Models. Proceedings of the XI Latin-American Congress on Electricity Generation and Transmission–CLAGTEE 2015, Sao Paulo, Brazil. 5. Review and validation of photovoltaic solar simulation tools/software based on case study;Open Phys.,2022
|
|