Predictions of Surface Solar Radiation on Tilted Solar Panels using Machine Learning Models: A Case Study of Tainan City, Taiwan

Abstract

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/m 2 per year when the solar-panel tilt angle was 0 • (i.e., the non-tilted position) and peaked at 1.655 million w/m 2 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 • .

Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast

“…Finally, in the forecasting stage, the fuzzy inference method is used to select an adequately trained model for accurate forecast. The multilayer perceptron (MLP), random forests (RF), k-nearest neighbors (kNN), and linear regression (LR), algorithms were used for solar irradiance forecasting [11].…”

Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

“…Therefore, several studies have tried to estimate GHI empirically from the early 20th century until now from other climate variables, namely, Sunshine Duration (SD), Air Temperature (AT), cloud cover, and other variables, using the top-of-atmosphere irradiance on the horizontal surface (TOA) [6][7][8][9][10][11] and with linear regression models [12][13][14]. Recently, machine learning approaches have also been broadly used [15,16], which mostly include Artificial Neural Networks (ANNs), which will be discussed in a later section, Support Vector Machines, Random Forest [5,17,18] and some other machine learning models [19,20]. Some of these and other approaches have used satellite image data and interpolation techniques to cover the limitation of spatial resolution [3,[21][22][23].…”

“…Those papers demonstrated the role of ANNs in forecasting GHI at various time scales, and the role of SDDs and Cs as inputs to improve the model results. Another study has utilised machine learning algorisms to forecast GHI on a tilted panel based on several inputs namely climate variables, satellite data and solar position [19].…”

Data Science and Big Data in Energy Forecasting

“…Another interesting manuscript was published in the field of solar energy; predictions of surface solar radiation on tilted solar panels using machine learning models were reported in [12], using data from Taiwan as a case study. Also in Taiwan's Northeastern Coast, nearshore wave was predicted by means of data mining techniques during typhoons [13].…”