2022 journal article
Ultra-Short-Term Spatiotemporal Forecasting of Renewable Resources: An Attention Temporal Convolutional Network-Based Approach
IEEE TRANSACTIONS ON SMART GRID, 13(5), 3798–3812.
author keywords: Forecasting; Spatiotemporal phenomena; Predictive models; Renewable energy sources; Wind forecasting; Convolution; Task analysis; Attention mechanism; probabilistic forecasting; spatiotemporal forecasting; temporal convolutional networks
TL;DR:
An attention temporal convolutional network is proposed to perform the ultra-short-term spatiotemporal forecasting of renewable resources, which needs no domain knowledge and can be applied to different forecasting tasks such as solar generation and wind speed forecasting.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(Web of Science; OpenAlex)
Source: Web Of Science
Added: September 6, 2022
The rapid increase in the penetration of renewable energy resources characterized by high variability and uncertainty is bringing new challenges to the power system operation. To ensure the efficient and reliable operation of electric grid, an accurate and general short-term forecasting algorithm with interpretability is desired. Moreover, the extensive off-site information provided by the proliferation of new renewable plants stimulates the interests in the spatiotemporal forecasting. In this paper, an attention temporal convolutional network, which is built on stacked dilated causal convolutional networks and attention mechanisms, is proposed to perform the ultra-short-term spatiotemporal forecasting of renewable resources. Compared with the existing spatiotemporal forecasting methods, the presented model needs no domain knowledge and can be applied to different forecasting tasks such as solar generation and wind speed forecasting. The attention mechanism improves the interpretability. The algorithm can be used to produce both point and probabilistic forecasts. Numerical results on the data sets from National Renewable Energy Laboratory show superior performance over five baselines, in terms of skill scores. Compared with the baselines, the average improvements of accuracy introduced by the proposed method for the point and probabilistic forecasting are 15.08% and 15.85%, respectively.