Abstract
In this paper, forecasting models were constructed to estimate surface solar radiation on an hourly basis and the solar irradiance received by solar panels at different tilt angles, to enhance the capability of photovoltaic systems by estimating the amount of electricity they generate, thereby improving the reliability of the power they supply. The study site was Tainan in southern Taiwan, which receives abundant sunlight because of its location at a latitude of approximately 23°. Four forecasting models of surface solar irradiance were constructed, using the multilayer perceptron (MLP), random forests (RF), k-nearest neighbors (kNN), and linear regression (LR), algorithms, respectively. The forecast horizon ranged from 1 to 12 h. The findings are as follows: first, solar irradiance was effectively estimated when a combination of ground weather data and solar position data was applied. Second, the mean absolute error was higher in MLP than in RF and kNN, and LR had the worst predictive performance. Third, the observed total solar irradiance was 1.562 million w/m2 per year when the solar-panel tilt angle was 0° (i.e., the non-tilted position) and peaked at 1.655 million w/m2 per year when the angle was 20–22°. The level of the irradiance was almost the same when the solar-panel tilt angle was 0° as when the angle was 41°. In summary, the optimal solar-panel tilt angle in Tainan was 20–22°.
Highlights
The sun is one of the main sources of energy on Earth
The purpose of this paper is to forecast surface solar radiation using machine learning models on an hourly basis and the solar irradiance received by solar panels at different tilt angles, to enhance the capability of photovoltaic systems by estimating the amount of electricity they generate, thereby improving the reliability of the power they supply
multilayer perceptron (MLP), random forests (RF), and k-nearest neighbors (kNN) were comparable in all the measures, suggesting that dataset combinations
Summary
The sun is one of the main sources of energy on Earth. As nonrenewable energy resources on the planet are being depleted and renewable ones are increasingly in demand, the development potential of solar energy warrants attention. Solar energy provides an environmentally friendly solution to generating electricity. Located at the latitude 22–25◦ N, Taiwan receives a long duration of sunshine and stands at a small sunlight deflection angle. The world’s second largest producer of silicon-wafer solar cells, it is well-positioned to develop solar-energy technologies. Taiwanese’s Bureau of Energy has launched a nationwide initiative—Million Rooftop PVs—to promote the installation of solar photovoltaic (PV) on the rooftops of residential buildings. Public demand for PV systems remains robust; the total installed capacity of solar PV power generation units was 847 MW, and projected to be 2120 MW by 2020 and 3100 MW by 2030
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