Abstract
Photovoltaic systems have become an important source of renewable energy generation. Because solar power generation is intrinsically highly dependent on weather fluctuations, predicting power generation using weather information has several economic benefits, including reliable operation planning and proactive power trading. This study builds a model that predicts the amounts of solar power generation using weather information provided by weather agencies. This study proposes a two-step modeling process that connects unannounced weather variables with announced weather forecasts. The empirical results show that this approach improves a base approach by wide margins, regardless of types of applied machine learning algorithms. The results also show that the random forest regression algorithm performs the best for this problem, achieving an R-squared value of 70.5% in the test data. The intermediate modeling process creates four variables, which are ranked with high importance in the post-analysis. The constructed model performs realistic one-day ahead predictions.
Highlights
A smart grid is an electrical grid system that manages energy-related operations, including production, distribution, and consumption
The results identify (1) which machine learning method produces the best-performing model, (2) whether the predicted values for auxiliary variables created during the auxiliary modeling step have significant forecasting performance for solar power generation, and (3) how much each independent variable among weather forecast and weather observation contributes to prediction performance
This study proposes a two-step approach to solar power generation prediction to fully exploit the information contained in the weather data
Summary
A smart grid is an electrical grid system that manages energy-related operations, including production, distribution, and consumption. Efficient smart grid operations are aided by reliable power supply planning. Planning on renewable energy operations, such as sunlight, wind, tides, and geothermal energy, involves a unique (unique class) class of prediction problem because these natural energy sources are intermittent and uncontrollable, due to fluctuating weather conditions [1]. The photovoltaic geographic information system (PVGIS) [2] provides climate data and the performance assessment tools of photovoltaic (PV) technology mainly for Europe and Africa. Many studies are conducted to predict the level of future solar irradiance or PV power generation in solar plants using weather information. Sources of weather information include both measured weather records and weather forecasts. This study proposes a novel two-step prediction process for PV power generation using both weather records and weather forecasts. This study demonstrates the philosophy of data-driven modeling with as much relevant data as possible to improve model performance
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