Energy forecast for a cogeneration system using dynamic factor models
Abstract
Cogeneration is used in different sectors of industry and it allows that two types of energy to be efficiently obtained from a single source. Accurate predictions are fundamental to optimize energy production, considering the variability that occurs in the daily market. This study adjusts and predicts cogeneration using real data from a Spanish energy technology center, using dynamic factor analysis methodology and incorporating covariates such as temperature and relative humidity. A comparative analysis is performed to evaluate the improvements achieved by implementing cluster-structured dynamic models versus other methods. Furthermore, a robust interpolation method has been implemented to handle missing data in both the main variable and the covariates.
Description
Cogeneration is widely applied across various industrial sectors, enabling the efficient generation of two types of energy from a single source. To optimize energy production, precise forecasting is crucial due to daily market fluctuations. This research focuses on modeling and forecasting cogeneration using real-world data from a Spanish energy technology center. The study employs dynamic factor analysis, integrating factors like temperature and relative humidity as covariates. A comparative analysis assesses the benefits of using cluster-structured dynamic models versus traditional methods. Additionally, a robust interpolation technique has been developed to manage missing data in both the primary variables and the covariates.
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