Estimating Solar and Wind Power Production using Computer Vision Deep Learning Techniques on Weather Maps

TL;DR

This paper introduces a ResNet-inspired deep learning model that estimates solar and wind power production from weather maps, improving forecasting accuracy by capturing complex spatial and temporal weather dynamics.

Contribution

The paper presents a novel convolutional neural network model for renewable energy forecasting and provides a new California-focused weather dataset for research.

Findings

Model outperforms traditional deep learning methods in accuracy.

Produces smooth power production profiles with high-frequency details.

Demonstrates effectiveness on California weather data.

Abstract

Accurate renewable energy production forecasting has become a priority as the share of intermittent energy sources on the grid increases. Recent work has shown that convolutional deep learning models can successfully be applied to forecast weather maps. Building on this capability, we propose a ResNet-inspired model that estimates solar and wind power production based on weather maps. By capturing both spatial and temporal correlations using convolutional neural networks with stacked input frames, the model is designed to capture the complex dynamics governing these energy sources. A dataset that focuses on the state of California is constructed and made available as a secondary contribution of the work. We demonstrate that our novel model outperforms traditional deep learning techniques: it predicts an accurate power production profile that is smooth and includes high-frequency details.