Great energy predictor shootout II: A Bayesian nonlinear regression with multiple hyperparameters
- Waseda Univ., Tokyo (Japan). Dept. of Electrical, Electronics and Computer Engineering
- Tokyo Communication Network (Japan)
When nonlinearity is present, time series prediction becomes a difficult task. The ASHRAE Energy Predictor Shootout II competition problem is no exception; the difficulty is amplified because analytical equations for describing the dynamics are formidable, if not impossible. The problem belongs to a rather interesting class of problems that can arise in many practical situations. A Bayesian approach is taken in performing nonlinear regression on the ASHRAE Predictor Shootout II time series data. The Bayesian framework enables one to perform the regression in a hierarchical manner: (1) level 1: estimation of the parameters; (2) level 2: estimation of hyperparameters; and (3) level 3: model comparison. The prediction results appear to be reasonable.
- OSTI ID:
- 433742
- Report Number(s):
- CONF-960606-; TRN: IM9709%%176
- Resource Relation:
- Conference: 1996 annual meeting of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), Inc., San Antonio, TX (United States), 22-26 Jun 1996; Other Information: PBD: 1996; Related Information: Is Part Of ASHRAE transactions 1996: Volume 102, Part 2; PB: 836 p.
- Country of Publication:
- United States
- Language:
- English
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