Research on Prediction of Solar Power Considering the Methods of Statistical and Machine Learning – Based on the Data of Australian Solar Power Market

In this paper, we use the methods of machine learning and traditional time series to predict solar power generation, which is based on the Australia Market Data. In the paper, we analyze Ausgrid’s Solar by using long and short-term memory (LSTM) methods and time series models (multiple regression models with related errors) to accurately estimate the parameters of photovoltaic (PV) array models, which is using the data of household electricity consumption from July 1, 2010, to June 30, 2013. The results show that the regression model with correlated errors is better than the machine learning-based LSTM algorithm, which is based on the differential MSE performance. The final prediction accuracy rate is as high as 98%, so the regression model can accurately predict solar power generation. Download paper here

Recommended citation: Zhao, P., & Tian, W. (2022, June). Research on prediction of solar power considering the methods of statistical and machine learning–based on the data of Australian solar power market. In IOP Conference Series: Earth and Environmental Science (Vol. 1046, No. 1, p. 012006). IOP Publishing. https://iopscience.iop.org/article/10.1088/1755-1315/1046/1/012006/pdf