Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting

TL;DR

This paper introduces a deep learning-based spatio-temporal wind speed forecasting method using RNNs and graph modeling, providing simultaneous forecasts for multiple locations, which enhances short-term prediction accuracy for renewable energy applications.

Contribution

The work presents a novel framework that models spatio-temporal data with graphs and produces concurrent forecasts for all nodes, improving upon existing benchmark models.

Findings

Significant improvement over benchmark models in wind speed prediction.

Effective modeling of spatial interactions among wind farms.

Simultaneous multi-node forecasting capability.

Abstract

The paper presents a spatio-temporal wind speed forecasting algorithm using Deep Learning (DL)and in particular, Recurrent Neural Networks(RNNs). Motivated by recent advances in renewable energy integration and smart grids, we apply our proposed algorithm for wind speed forecasting. Renewable energy resources (wind and solar)are random in nature and, thus, their integration is facilitated with accurate short-term forecasts. In our proposed framework, we model the spatiotemporal information by a graph whose nodes are data generating entities and its edges basically model how these nodes are interacting with each other. One of the main contributions of our work is the fact that we obtain forecasts of all nodes of the graph at the same time based on one framework. Results of a case study on recorded time series data from a collection of wind mills in the north-east of the U.S. show that the proposed DL-based forecasting algorithm significantly improves the short-term forecasts compared to a set of widely-used benchmarks models.