Efficient Deterministic Renewable Energy Forecasting Guided by Multiple-Location Weather Data

TL;DR

This paper introduces a novel deep learning approach using multi-location weather data to improve the accuracy of deterministic wind and solar energy forecasts, aiding better grid integration.

Contribution

It proposes a U-shaped Temporal Convolutional Auto-Encoder with Multi-sized Kernels Spatio-Temporal Attention for multi-site renewable energy forecasting, without requiring prior location knowledge.

Findings

Outperforms state-of-the-art forecasting methods

Effective across multiple datasets for day-ahead predictions

Demonstrates robustness in wind and solar energy scenarios

Abstract

Electricity generated from renewable energy sources has been established as an efficient remedy for both energy shortages and the environmental pollution stemming from conventional energy production methods. Solar and wind power are two of the most dominant renewable energy sources. The accurate forecasting of the energy generation of those sources facilitates their integration into electric grids, by minimizing the negative impact of uncertainty regarding their management and operation. This paper proposes a novel methodology for deterministic wind and solar energy generation forecasting for multiple generation sites, utilizing multi-location weather forecasts. The method employs a U-shaped Temporal Convolutional Auto-Encoder (UTCAE) architecture for temporal processing of weather-related and energy-related time-series across each site. The Multi-sized Kernels convolutional Spatio-Temporal Attention (MKST-Attention), inspired by the multi-head scaled-dot product attention mechanism, is also proposed aiming to efficiently transfer temporal patterns from weather data to energy data, without a priori knowledge of the locations of the power stations and the locations of provided weather data. The conducted experimental evaluation on a day-ahead solar and wind energy forecasting scenario on five datasets demonstrated that the proposed method achieves top results, outperforming all competitive time-series forecasting state-of-the-art methods.