Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data

Regional solar power forecasting, which involves predicting the total power generation from all rooftop photovoltaic (PV) systems in a region holds significant importance for various stakeholders in the energy sector to ensure a stable electricity supply. However, the vast amount of solar power generation and weather time series from geographically dispersed locations that need to be considered in the forecasting process makes accurate regional forecasting challenging. Therefore, previous studies have limited the focus to either forecasting a single time series (i.e., aggregated time series) which is the addition of all solar generation time series in a region, disregarding the location-specific weather effects or forecasting solar generation time series of each PV site (i.e., individual time series) independently using location-specific weather data, resulting in a large number of forecasting models. In this work, we propose two new deep-learning-based regional forecasting methods that can effectively leverage both types of time series (aggregated and individual) with weather data in a region. We propose two hierarchical temporal convolutional neural network architectures (HTCNN A1 and A2) and two new strategies to adapt HTCNNs for regional solar power forecasting. In the first strategy, we explore generating a regional forecast using a single HTCNN. In the second, we divide the region into multiple sub-regions based on weather information and train separate HTCNNs for each sub-region; the forecasts of each sub-region are then added to generate a regional forecast. The proposed work is evaluated using a large dataset collected over a year from 101 locations across Western Australia to provide a day ahead forecast at an hourly time resolution which involves forecasting a horizon of 18 h. We compare our approaches with well-known alternative methods, including long short-term memory networks and convolution neural networks, and show that proposed HTCNN-based approaches require fewer individually trained networks. Furthermore, the sub-region-based HTCNN-based approach achieves a forecast skill score of 40.2% and reduces a statistically significant forecast error by 6.5% compared to the best-performing counterpart. Our results indicate that the proposed approaches are well-suited for forecasting applications covering large regions containing many individual solar PV systems in different locations.