Abstract:
Solar power is one of the most rapidly growing carbon-free power generation solutions. It is considered as a key element in the fight against global climate crisis; however, rapid expansion in the distributed PV power, i.e. plants with less than 1 MW capacity, brings about some problems to the electricity markets. Spatially dispersed positioning of hundreds of plants cause significant variations in the power supply where trading operations depend on accurate forecasts of the future production. In this study, several deep learning techniques are implemented for the day-ahead solar power fore casting problem to predict the aggregated output of over a thousand solar stations distributed over a large area in the Central Anatolian Region. Four different archi tectures in the literature are adapted to the spatiotemporal numerical weather predic tion (NWP) data, along with the proposed parallel locally-connected long short-term memory (PLC-LSTM) architecture. All models are put through a distributed heuris tic hyperparameter tuning process using multiple graphical processing units (GPUs). Best-performing trials of each model are selected according to their validation results and compared with each other, together with persistence and an individual plant naive model as benchmarks. The results show that deep learning models work considerably well in spatiotemporal PV forecasting problem, compared to benchmarks. Also, it is seen that even simple architectures can perform close to models with a higher degree of complexity, when a good combination of parameters is obtained with a thorough search procedure. Although there is not a single dominating architecture prevailing in all kinds of performance metrics, PLC-LSTM shows promising results by finding a sweet spot of complexity between the shared-weight and fully-connected architectures, considering the bias and variance of the corresponding models.
