Appliance Level Short-term Load Forecasting via Recurrent Neural Network
This work addresses the need for more granular load forecasting in power systems, which is incremental as it applies an existing LSTM method to a new, appliance-level data context.
The paper tackles the problem of short-term load forecasting at the individual appliance level for residential customers, using a recurrent neural network with LSTM to predict power consumption and incorporating past prediction errors to improve accuracy, with numerical tests on real-world datasets showing improvements over existing methods.
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.