Day-ahead regional solar power forecasting with hierarchical temporal
  convolutional neural networks using historical power generation and weather
  data
Maneesha Perera Julian De Hoog Kasun Bandara Damith Senanayake Saman Halgamuge
Regional solar power forecasting, which involves predicting the total power
generation from all rooftop photovoltaic systems in a region holds significant
importance for various stakeholders in the energy sector. 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 work has 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 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)
and two strategies to adapt HTCNNs for regional solar power forecasting. At
first, we explore generating a regional forecast using a single HTCNN. Next, 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. We compare our approaches with
well-known alternative methods and show that the sub-region HTCNN requires
fewer individual networks and achieves a forecast skill score of 40.2% reducing
a statistically significant error by 6.5% compared to the best counterpart.