Neural Network Middle-Term Probabilistic Forecasting of Daily Power Consumption
| Autor: | Roberto Baviera, Michele Azzone |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Economics and Econometrics 050208 finance Artificial neural network Computer science Strategy and Management 05 social sciences Machine Learning (stat.ML) Function (mathematics) Confidence interval Electric utility Methodology (stat.ME) General Energy Autoregressive model Statistics - Machine Learning Test set 0502 economics and business Econometrics Feature (machine learning) FOS: Electrical engineering electronic engineering information engineering Probabilistic forecasting 050207 economics Electrical Engineering and Systems Science - Signal Processing Statistics - Methodology |
| DOI: | 10.48550/arxiv.2006.16388 |
| Popis: | Middle-term horizon (months to a year) power consumption prediction is a major challenge in the energy sector, particularly when probabilistic forecasting is considered. We propose a new modeling approach that incorporates trend, seasonality and weather conditions as explicative variables in a shallow neural network with an autoregressive feature. Applying it to the daily power consumption in New England, we obtain excellent results for the density forecast on the one-year test set. We verified the quality of the power consumption probabilistic forecasting achieved not only by comparing the results with other standard models for density forecasting but also by considering measures that are frequently used in the energy sector, such as the pinball loss function and confidence interval backtesting. |
| Databáze: | OpenAIRE |
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