Renewable Energy Production Forecasting: A Comparative Machine Learning Analysis
Kazi Md Shahiduzzaman1, Md Noor Jamal2, Md. Rashed Ibn Nawab3
1Kazi Md Shahiduzzaman *, Department of Electrical and Electronic Engineering, Jatiya Kazi Nazrul Islam University, Mymensingh, Bangladesh.
2Md Noor Jamal, School of Electronics, Information and Communication, Huazhong University of Science and Technology, Wuhan, China.
3Md. Rashed Ibn Nawab, School of Computer Science and Technology, Northwestern Polytechnical University, China.
Manuscript received on May 24, 2021. Revised Manuscript received on July 19, 2021. Manuscript published on August 30, 2021.| PP: 11-18 | Volume-10 Issue-6, August 2021 | Retrieval Number: 100.1/ijeat.E26890610521 | DOI: 10.35940/ijeat.E2689.0810621
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: As renewable energy has become increasingly popular worldwide, while solar and wind energy has been the leading source of renewable energy up to now, the accuracy of renewable energy forecasts is challenge for the planning, management, and operations of the power system. However, due to the intermediate and frenzied nature of renewable energy data, this is a most challenging task. This study provides a comprehensive and complete review of the renewable energy forecast based on different machine learning algorithms to explore effectiveness, efficiency, competence, and application potential. In this work, we have built time series renewable energy forecasting model with Support Vector Machine (SVM), Linear Regression (LR), and Long Short-Term Memory (LSTM) on twelve (12) countries. The experimental results are very interesting. For example, SVM based forecasting model is a better fit for the countries with small mean and standard deviation while linear regression-based methods show a bit better result in case of larger mean and standard deviation. Meanwhile, LSTM based models provide smoother regular-shaped forecasting. We can forecast two years of daily renewable energy production with these forecasting models. The point should be noted that we have developed different models for different countries. We have able to reach a Root Mean Square (RMS) value of 3.1 38 with SVM based model
Keywords: Renewable Energy Forecasting, Support Vector Machine (SVM), Linear Regression (LR), Long Short-Term Memory (LSTM), Time Series Forecasting.