A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features

With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance application of a smart grid (SG). Other than aggregated residential loads in large scale, it still urgent problem improve accuracy individual energy users due high volatility and uncertainty. However, as important variable that affects consumption pattern, influence weather factors on prediction rarely studied. In this paper, we review related research introduce short-term model based long memory (LSTM) recurrent neural network with features input.