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

TLDR

Short‑term load forecasting for individual residential customers is increasingly critical as power systems transition to more intelligent, flexible grids with higher renewable penetration, yet it remains challenging due to high volatility and uncertainty. The study proposes an LSTM‑based framework to improve short‑term load forecasting for individual households. The framework was evaluated on a public residential smart‑meter dataset and benchmarked against multiple state‑of‑the‑art load‑forecasting methods. The LSTM model achieved superior short‑term forecasting accuracy compared to the listed rival algorithms.

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.