Local-Global Methods for Generalised Solar Irradiance Forecasting
- URL: http://arxiv.org/abs/2303.06010v2
- Date: Mon, 10 Jul 2023 15:33:54 GMT
- Title: Local-Global Methods for Generalised Solar Irradiance Forecasting
- Authors: Timothy Cargan, Dario Landa-Silva, Isaac Triguero
- Abstract summary: 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.
- Score: 1.4452289368758378
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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