# Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

“Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network” (2017), by Weicong Kong et al. Engineering research paper. Original abstract below, unchanged. No free PDF was found.

- Authors: Weicong Kong, Zhao Yang Dong, Youwei Jia, David John Hill, Yan Hui Xu, Yuan Zhang
- Published: September 18, 2017
- Field: Engineering
- DOI: https://doi.org/10.1109/tsg.2017.2753802
- Source: https://doi.org/10.1109/tsg.2017.2753802

## Abstract

As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.

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