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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Article
An Explanatory Study Approach, Using Machine Learning to Forecast Solar Energy Outcome
Author(s)
Agada Ihuoma Nkechi and Nagata Yasunori
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DOI:10.17265/1934-8975/2022.02.004
Affiliation(s)
Department of Electrical and Electronics Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
ABSTRACT
AI (artificial intelligence) techniques play a crucially important role in predicting the expected energy outcome and its performance, analysis, modeling and control of renewable energy. Solar energy usage has grown exponentially over the years. In the face of global energy consumption and increased depletion of most fossil fuel, the world is faced with the challenges of meeting the ever-increasing energy demands, also utility companies who provide solar energy have a challenge of unstable input of solar energy to the grid due to its intermittent nature, unlike other sources, hence the difference between expected generation and actual generation, demand and supply can lead to an unbalanced grid. Therefore, incorporating accurately machine learning technology to predict the expected outcome of solar energy from the intermittent solar radiation will be crucial to keep a balance grid operation between supply and demand, production planning and energy management especially during installations of a photovoltaic power plant. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems which are complicated and involve large computer power, differential equations, and time series. Also having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, we employ the Anaconda Navigator (Jupyter Notebook) for machine learning which can combine large amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turn enables the balance between supply and demand on loads, efficient operation of the utility company as well as enhances power production planning and management.
KEYWORDS
AI, backward elimination, data mining, machine learning, linear regression, solar energy.
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