Abstract
Precise and accurate prediction of solar photovoltaic (PV) generation plays a major role in developing plans for the supply and demand of power grid systems. Most previous studies on the prediction of solar PV generation employed only weather data composed of numerical text data. The numerical text weather data can reflect temporal factors, however, they cannot consider the movement features related to the wind direction of the spatial characteristics, which include the amount of both clouds and particulate matter (PM) among other weather features. This study aims developing a hybrid spatio-temporal prediction model by combining general weather data and data extracted from satellite images having spatial characteristics. A model for hourly prediction of solar PV generation is proposed using data collected from a solar PV power plant in Incheon, South Korea. To evaluate the performance of the prediction model, we compared and performed ARIMAX analysis, which is a traditional statistical time-series analysis method, and SVR, ANN, and DNN, which are based on machine learning algorithms. The models that reflect the temporal and spatial characteristics exhibited better performance than those using only the general weather numerical data or the satellite image data.
Highlights
The issue of climate change caused by carbon emission and the depletion of fossil fuels is emerging worldwide
Conventional meteorological data are composed of numerical text data and have a continuity of time, but it is difficult to reflect the effects of spatial characteristics, such as the movement of clouds and particulate matter (PM) moving by the wind direction, as raw data in the prediction model
Data from a solar PV power plant located in Incheon, South Korea, was used as the test target, and the entire test was conducted on an hourly basis from January to December 2015
Summary
The issue of climate change caused by carbon emission and the depletion of fossil fuels is emerging worldwide. Solar PV power generation requires a large installation area due to its low energy density, and the amount of power generated fluctuates with meteorological factors such as a change in irradiance due to clouds or particulate matter (PM) [6,7]. This phenomenon increases the complexity of the plan for stable supply and demand of power systems, it mostly disrupts the schedule for power grid operations. Accurate prediction of the power generation of renewable energy sources is very important in establishing an efficient power supply and demand plan
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