Demand Forecasting in Smart Grid Using Long Short-Term Memory

TL;DR

This paper demonstrates that an LSTM neural network model significantly improves power demand forecasting accuracy in smart grids compared to traditional statistical methods, enabling more efficient demand response systems.

Contribution

It introduces an LSTM-based neural network model for power demand forecasting and compares its performance to traditional methods, showing superior accuracy.

Findings

LSTM model achieves a mean absolute percentile error of 1.22

LSTM outperforms Auto-Regressive models in demand prediction

Neural networks reduce forecast error significantly

Abstract

Demand forecasting in power sector has become an important part of modern demand management and response systems with the rise of smart metering enabled grids. Long Short-Term Memory (LSTM) shows promising results in predicting time series data which can also be applied to power load demand in smart grids. In this paper, an LSTM based model using neural network architecture is proposed to forecast power demand. The model is trained with hourly energy and power usage data of four years from a smart grid. After training and prediction, the accuracy of the model is compared against the traditional statistical time series analysis algorithms, such as Auto-Regressive (AR), to determine the efficiency. The mean absolute percentile error is found to be 1.22 in the proposed LSTM model, which is the lowest among the other models. From the findings, it is clear that the inclusion of neural network in predicting power demand reduces the error of prediction significantly. Thus, the application of LSTM can enable a more efficient demand response system.