Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

• Published in: Energies (Basel) Vol. 11; no. 4; p. 862
• Main Authors: Prakash, Anand, Xu, Susu, Rajagopal, Ram, Noh, Hae
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2018
• Subjects: Bayesian analysis; Black boxes; building energy load forecasting; Buildings; Computer applications; Computing time; Covariance; Electricity consumption; Energy consumption; Energy resources; Energy sources; Forecasting; Gaussian process; Gaussian Process Regression; HVAC load; Kernel Model; lighting load; Mathematical models; Measuring instruments; Normal distribution; Regression analysis; Statistical analysis; Utilities
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en11040862

Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR) that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University) for both short- and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error).