Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting
By Amir Ghaderi, Borhan Molazem Sanandaji, Faezeh Ghaderi. 2017
115 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Time-Series & Forecasting
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Citations in the corpus
- Bayesian Temporal Factorization for Multidimensional Time Series Prediction (2019, background): “Third, we would like to integrate recent advances in deep learning to better capture the complex and non-linear dynamics in modern time series data [39], [40], [41], [42], [43], [44], [45], [46].”