RNN(p) for Power Consumption Forecasting

TL;DR

This paper introduces RNN(p), a recurrent neural network model designed for power consumption forecasting, demonstrating high accuracy, interpretability, and efficient training strategies suitable for energy sector applications.

Contribution

The paper presents RNN(p), a novel structured feedback neural network model that generalizes ARX models, with a comparative analysis of learning algorithms and practical applications in power forecasting.

Findings

RNN(p) achieves high forecasting accuracy.

The model maintains interpretability.

Efficient training strategies are developed.

Abstract

An elementary Recurrent Neural Network that operates on p time lags, called an RNN(p), is the natural generalisation of a linear autoregressive model ARX(p). It is a powerful forecasting tool for variables displaying inherent seasonal patterns across multiple time scales, as is often observed in energy, economic, and financial time series. The architecture of RNN(p) models, characterised by structured feedbacks across time lags, enables the design of efficient training strategies. We conduct a comparative study of learning algorithms for these models, providing a rigorous analysis of their computational complexity and training performance. We present two applications of RNN(p) models in power consumption forecasting, a key domain within the energy sector where accurate forecasts inform both operational and financial decisions. Experimental results show that RNN(p) models achieve excellent forecasting accuracy while maintaining a high degree of interpretability. These features make them well-suited for decision-making in energy markets and other fintech applications where reliable predictions play a significant economic role.