Chiller Load Forecasting Using Hyper-Gaussian Nets

Manuel R. Arahal, Manuel G. Ortega and Manuel G. Satué
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Manuel R. Arahal: Systems Engineering and Automation Department, University of Seville, 41092 Seville, Spain
Manuel G. Ortega: Systems Engineering and Automation Department, University of Seville, 41092 Seville, Spain
Manuel G. Satué: Systems Engineering and Automation Department, University of Seville, 41092 Seville, Spain

Energies, 2021, vol. 14, issue 12, 1-15

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 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.

Keywords: energy consumption prediction; time-series forecasting; neural approximation; hyper-gaussian (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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