Comparison of Two Modified Deterministic Lstm Models With A Probabilistic Lstm Model For A Day-Ahead Forecasting of Electricity Demands

This paper presents two simple modifications of a standard deterministic LSTM method, which are specifically implemented for the probabilistic day-ahead forecasting of electricity demands. Both modifications feature a two-stage model, where a standard deterministic LSTM method is built in the first stage, and then used in the second stage in a hindcasting-based evaluation of the model uncertainty (first model modification) and model parameter uncertainty (second model modification). The estimated uncertainty ranges from both modified models are added to the deterministic predictions of the first-stage LSTM model, in order to provide probabilistic demand forecasting. The two presented modified deterministic LSTM models are discussed in detail and compared with a single-stage probabilistic LSTM forecasting model, in order to evaluate their performance and suitability for a day-ahead probabilistic electricity demand forecasting.