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.
Highlights
Renewable energy sources are widely available and have the potential to meet the energy needs of the entire human race
Interstate creates a difference in energy production which implies that timing, climate change, changing weather conditions, and geographical location are affected in the term of power generation from the power source
We have considered Long Short-Term Memory (LSTM), Support Vector Machine (SVM) and Linear regression (LR)
Summary
Renewable energy sources are widely available and have the potential to meet the energy needs of the entire human race. Retrieval Number: 100.1/ijeat.E26890610521 DOI:10.35940/ijeat.E2689.0810621 Journal Website: www.ijeat.org These limitations lead to several interesting energy forecasting features, which included the need for accurate accuracy and data collection. There are cases where on-site measures of wind and solar irradiation are not possible and other meteorological variables such as temperature and humidity are not accessible and only historical statistics are available In such cases, data-driven models can be used for forecasting. Studying solar power, wind power, and forecasting use multiple sources in their proposed forecasting method, and different processes in a particular source They try to combine all the applied predictions instead of choosing the best one. In this article we have compare between different machine learning algorithms for generation of renewable energy over a daytime period with the highest accuracy This provides the most accurate and efficient power dispatch for energy management systems.
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