Chiller Load Forecasting Using Hyper-Gaussian Nets
|Author/s||Ruiz Arahal, Manuel
Ortega Linares, Manuel Gil
Garrido Satué, Manuel
|Department||Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática|
|Abstract||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 ...
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.
|Funding agencies||European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
Agencia Estatal de Investigación. España
|Citation||Ruiz Arahal, M., Ortega Linares, M.G. y Garrido Satué, M. (2021). Chiller Load Forecasting Using Hyper-Gaussian Nets. Energies, 14 (12), 3479.|
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