PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Predicting the Purchase of Electricity Prices for Renewable Energy Sources Based on Polish Power Grids Data Using Deep Learning Models for Controlling Small Hybrid PV Microinstallations
Version 1
: Received: 18 November 2023 / Approved: 20 November 2023 / Online: 20 November 2023 (14:03:57 CET)
How to cite:
Pikus, M.; Wąs, J. Predicting the Purchase of Electricity Prices for Renewable Energy Sources Based on Polish Power Grids Data Using Deep Learning Models for Controlling Small Hybrid PV Microinstallations. Preprints2023, 2023111248. https://doi.org/10.20944/preprints202311.1248.v1
Pikus, M.; Wąs, J. Predicting the Purchase of Electricity Prices for Renewable Energy Sources Based on Polish Power Grids Data Using Deep Learning Models for Controlling Small Hybrid PV Microinstallations. Preprints 2023, 2023111248. https://doi.org/10.20944/preprints202311.1248.v1
Pikus, M.; Wąs, J. Predicting the Purchase of Electricity Prices for Renewable Energy Sources Based on Polish Power Grids Data Using Deep Learning Models for Controlling Small Hybrid PV Microinstallations. Preprints2023, 2023111248. https://doi.org/10.20944/preprints202311.1248.v1
APA Style
Pikus, M., & Wąs, J. (2023). Predicting the Purchase of Electricity Prices for Renewable Energy Sources Based on Polish Power Grids Data Using Deep Learning Models for Controlling Small Hybrid PV Microinstallations. Preprints. https://doi.org/10.20944/preprints202311.1248.v1
Chicago/Turabian Style
Pikus, M. and Jarosław Wąs. 2023 "Predicting the Purchase of Electricity Prices for Renewable Energy Sources Based on Polish Power Grids Data Using Deep Learning Models for Controlling Small Hybrid PV Microinstallations" Preprints. https://doi.org/10.20944/preprints202311.1248.v1
Abstract
In the quest for sustainable energy solutions, predicting electricity prices for renewable energy sources plays a pivotal role in efficient resource allocation and decision-making. This article presents a novel approach to forecasting electricity prices for renewable energy sources using deep learning models, leveraging historical data from the Power System Operator (PSE). The proposed methodology encompasses data collection, preprocessing, feature engineering, model selection, training, and evaluation. By harnessing the power of recurrent neural networks (RNNs) and other advanced deep learning architectures, the model captures intricate temporal relationships, weather patterns, and demand fluctuations that impact renewable energy prices. The study demonstrates the applicability of this approach through empirical analysis, showcasing its potential to enhance energy market predictions and aid in the transition to more sustainable energy systems. The outcomes underscore the importance of accurate renewable energy price predictions in fostering informed decision-making and facilitating the integration of renewable sources into the energy landscape. As governments worldwide prioritize renewable energy adoption, this research contributes to the arsenal of tools driving the evolution towards a cleaner and more resilient energy future.
Keywords
AI; Energy Price Forecasting; LSTM; DNN
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
