Chiller Load Forecasting Using Hyper-Gaussian Nets

Autor:	Manuel R. Arahal, Manuel G. Ortega, Manuel G. Satué
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	energy consumption prediction time-series forecasting neural approximation hyper-gaussian Technology
Zdroj:	Energies, Vol 14, Iss 12, p 3479 (2021)
Druh dokumentu:	article
ISSN:	14123479 1996-1073
DOI:	10.3390/en14123479
Popis:	Energy load forecasting for optimization of chiller operation is a topic that has been receiving increasing attention in recent years. From an engineering perspective, the methodology for designing and deploying a forecasting system for chiller operation should take into account several issues regarding prediction horizon, available data, selection of variables, model selection and adaptation. In this paper these issues are parsed to develop a neural forecaster. The method combines previous ideas such as basis expansions and local models. In particular, hyper-gaussians are proposed to provide spatial support (in input space) to models that can use auto-regressive, exogenous and past errors as variables, constituting thus a particular case of NARMAX modelling. Tests using real data from different world locations are given showing the expected performance of the proposal with respect to the objectives and allowing a comparison with other approaches.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/6f9ef3b6c89c47f881d9f56245589ada Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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