Automated Spatio-Temporal Weather Modeling for Load Forecasting

TL;DR

This paper presents a deep learning approach to automatically model complex spatio-temporal weather patterns to improve electricity load forecasting accuracy, addressing the spatial and temporal variability of meteorological influences.

Contribution

It introduces an automated deep neural network methodology for spatio-temporal weather modeling, enhancing load forecasting beyond fixed models used in current state-of-the-art approaches.

Findings

Improved load forecasting accuracy on French national data.

Demonstrated the effectiveness of deep learning for spatio-temporal weather feature extraction.

Potential applicability to renewable energy production forecasting.

Abstract

Electricity is difficult to store, except at prohibitive cost, and therefore the balance between generation and load must be maintained at all times. Electricity is traditionally managed by anticipating demand and intermittent production (wind, solar) and matching flexible production (hydro, nuclear, coal and gas). Accurate forecasting of electricity load and renewable production is therefore essential to ensure grid performance and stability. Both are highly dependent on meteorological variables (temperature, wind, sunshine). These dependencies are complex and difficult to model. On the one hand, spatial variations do not have a uniform impact because population, industry, and wind and solar farms are not evenly distributed across the territory. On the other hand, temporal variations can have delayed effects on load (due to the thermal inertia of buildings). With access to observations from different weather stations and simulated data from meteorological models, we believe that both phenomena can be modeled together. In today's state-of-the-art load forecasting models, the spatio-temporal modeling of the weather is fixed. In this work, we aim to take advantage of the automated representation and spatio-temporal feature extraction capabilities of deep neural networks to improve spatio-temporal weather modeling for load forecasting. We compare our deep learning-based methodology with the state-of-the-art on French national load. This methodology could also be fully adapted to forecasting renewable energy production.