Appliance Level Short-term Load Forecasting via Recurrent Neural Network

TL;DR

This paper introduces an LSTM-based short-term load forecasting method for individual appliances, improving prediction accuracy by leveraging appliance-specific consumption patterns and past errors, with demonstrated superior performance on real data.

Contribution

The paper presents a novel appliance-level STLF algorithm using LSTM that incorporates error tracking to enhance prediction accuracy, filling a gap in existing aggregated load forecasting research.

Findings

Outperforms existing LSTM-based methods in accuracy

Effectively captures appliance-specific consumption patterns

Demonstrates improved predictions on real-world datasets

Abstract

Accurate load forecasting is critical for electricity market operations and other real-time decision-making tasks in power systems. This paper considers the short-term load forecasting (STLF) problem for residential customers within a community. Existing STLF work mainly focuses on forecasting the aggregated load for either a feeder system or a single customer, but few efforts have been made on forecasting the load at individual appliance level. In this work, we present an STLF algorithm for efficiently predicting the power consumption of individual electrical appliances. The proposed method builds upon a powerful recurrent neural network (RNN) architecture in deep learning, termed as long short-term memory (LSTM). As each appliance has uniquely repetitive consumption patterns, the patterns of prediction error will be tracked such that past prediction errors can be used for improving the final prediction performance. Numerical tests on real-world load datasets demonstrate the improvement of the proposed method over existing LSTM-based method and other benchmark approaches.