Photovoltaic System Performance Forecasting Using LSTM Neural Networks

• Main Author: Hamberg, Lukas
• Format: Others
• Language: English
• Published: Uppsala universitet, Institutionen för informationsteknologi 2021
• Subjects: Machine Learning; LSTM; neural networks; Photovoltaic systems; pv-systems; Deep learning; power output forecasting; Computer Sciences; Datavetenskap (datalogi)
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-455665

Deep learning has proven to be a valued contributor to recent technological advancements within energy systems. This thesis project explores methods of photovoltaic (PV) system power output forecasting through the utilization of long short-term memory (LSTM) neural networks. An encoder-decoder architecture (ED-LSTM) and a stacked vector output architecture (SVO-LSTM) were compared in terms of their ability to accurately produce power output forecasts with a 24-hour forecast horizon. The datasets which were used for model training were composed of historical meteorological observations and PV system power output readings. The results indicate that the encoder-decoder model and the stacked vector output model were somewhat equally skilled at producing power output forecasts. Best results were obtained by the encoder-decoder LSTM model which achieved a 26.63% improvement over a persistence model when trained on data sequences which preceded the forecast horizon, and a 44.96% improvement over a persistence model when the model was provided meteorological data from an oracle forecaster.