Loading...
Please wait, while we are loading the content...
Similar Documents
Chiller Load Forecasting Using Hyper-Gaussian Nets
| Content Provider | MDPI |
|---|---|
| Author | Manuel, Satué Arahal, Manuel Ortega, Manuel |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 3479 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en14123479 |
| Journal | Energies |
| Issue Number | 12 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-06-11 |
| Access Restriction | Open |
| Subject Keyword | Energies Industrial Engineering Energy Consumption Prediction Time-series Forecasting Neural Approximation Hyper-gaussian |
| Content Type | Text |
| Resource Type | Article |
