RNN(p) for Power Consumption Forecasting

• URL: http://arxiv.org/abs/2209.01378v2
• Date: Fri, 07 Nov 2025 10:43:05 GMT
• Title: RNN(p) for Power Consumption Forecasting
• Authors: Roberto Baviera, Pietro Manzoni,
• Abstract summary: 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)<n>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.<n> Experimental results show that RNN(p) models achieve excellent forecasting accuracy while maintaining a high degree of interpretability.
• Score: 0.896655290787267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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