使用長短時記憶人工智慧方法預估極短時分散式太陽能系統發電成效

To ensure the effective supply of solar energy and its quality, researchers are looking for better methods to improve the very short-term prediction accuracy of a solar energy harvesting system. In this study, weather information such as solar radiation and temperature, together with the experimental results of a distributed solar power harvesting system is used to train and test by an artificial intelligent, AI algorithm called the long short-term memory (LSTM) method. The LSTM model can assign different weighting coefficients to long-term and short-term memory data, and is particularly suitable for time-series data forecasting. The proposed multi-step and progressive LSTM models are able to provide the up-coming 5 to 10 minutes forecasting of the photovoltaic power system. The detail of the method and prediction results are reported, and the potential application of the machine learning algorithm will be discussed.