Journal article Open Access

# Renewable Energy Production Forecasting: A Comparative Machine Learning Analysis

Kazi Md Shahiduzzaman; Md Noor Jamal; Md. Rashed Ibn Nawab,

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:contributor>Blue Eyes Intelligence Engineering  and Sciences Publication (BEIESP)</dc:contributor>
<dc:creator>Kazi Md Shahiduzzaman</dc:creator>
<dc:creator>Md Noor Jamal</dc:creator>
<dc:creator>Md. Rashed Ibn Nawab,</dc:creator>
<dc:date>2021-08-30</dc:date>
<dc:description>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.</dc:description>
<dc:identifier>https://zenodo.org/record/5407608</dc:identifier>
<dc:identifier>10.35940/ijeat.E2689.0810621</dc:identifier>
<dc:identifier>oai:zenodo.org:5407608</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>issn:2249-8958</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:source>International Journal of Engineering and Advanced Technology (IJEAT) 10(6) 11-18</dc:source>
<dc:subject>Renewable Energy Forecasting, Support Vector Machine (SVM), Linear Regression (LR), Long Short-Term Memory (LSTM), Time Series Forecasting.</dc:subject>
<dc:subject>ISSN</dc:subject>
<dc:subject>Retrieval Number</dc:subject>
<dc:title>Renewable Energy Production Forecasting: A Comparative Machine Learning Analysis</dc:title>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>publication-article</dc:type>
</oai_dc:dc>

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