Multi-time-horizon Solar Forecasting Using Recurrent Neural Network

TL;DR

This paper introduces a unified RNN-based architecture for multi-time-horizon solar forecasting, improving prediction accuracy and enabling real-time, practical applications in smart grids.

Contribution

It proposes a novel end-to-end RNN architecture for simultaneous multi-horizon solar forecasting, outperforming traditional single-model approaches.

Findings

Lower root-mean-squared prediction error compared to previous methods

Effective for both short-term and long-term solar forecasting

Enables real-time multi-horizon predictions for smart grid applications

Abstract

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.