2 research outputs found
Local-Global Methods for Generalised Solar Irradiance Forecasting
As the use of solar power increases, having accurate and timely forecasts
will be essential for smooth grid operators. There are many proposed methods
for forecasting solar irradiance / solar power production. However, many of
these methods formulate the problem as a time-series, relying on near real-time
access to observations at the location of interest to generate forecasts. This
requires both access to a real-time stream of data and enough historical
observations for these methods to be deployed. In this paper, we propose the
use of Global methods to train our models in a generalised way, enabling them
to generate forecasts for unseen locations. We apply this approach to both
classical ML and state of the art methods. Using data from 20 locations
distributed throughout the UK and widely available weather data, we show that
it is possible to build systems that do not require access to this data. We
utilise and compare both satellite and ground observations (e.g. temperature,
pressure) of weather data. Leveraging weather observations and measurements
from other locations we show it is possible to create models capable of
accurately forecasting solar irradiance at new locations. This could facilitate
use planning and optimisation for both newly deployed solar farms and domestic
installations from the moment they come online. Additionally, we show that
training a single global model for multiple locations can produce a more robust
model with more consistent and accurate results across locations.Comment: 40 pages, 11 figure
Local-Global Methods for Generalised Solar Irradiance Forecasting
For efficient operation, solar power operators often require generation forecasts for multiple sites with varying data availability. Many proposed methods for forecasting solar irradiance / solar power production formulate the problem as a time-series, using current observations to generate forecasts. This necessitates a real-time data stream and enough historical observations at every location for these methods to be deployed. In this paper, we propose the use of Global methods to train gen-eralised models. Using data from 20 locations distributed throughout the UK, we show that it is possible to learn models without access to data for all locations, enabling them to generate forecasts for unseen locations. We show a single Global model trained on multiple locations can produce more consistent and accurate results across locations. Furthermore, by leveraging weather observations and measurements from other locations we show it is possible to create models capable of accurately forecasting irradiance at locations without any real-time data. We apply our approaches to both classical and 1 Springer Nature 2021 L A T E X template 2 Local-Global Methods for Generalised Solar Irradiance Forecasting state-of-the-art Machine Learning methods, including a Transformer architecture. We compare models using satellite imagery or point observations (temperature, pressure, etc.) as weather data. These methods could facilitate planning and optimisation for both newly deployed solar farms and domestic installations from the moment they come online