2016 ASHRAE Annual Conference

Buildings account for about 48 percent of the energy consumed in the United States. Of this energy, heating and cooling systems use about 55 percent, while lights and appliances use the other 35 percent of energy use of existing buildings. If energy-use trends continue, buildings will become the largest consumer of global energy by 2025. The development of building energy savings methods and models becomes apparently more necessary for a sustainable future. Most new buildings are equipped with modern building automation system BAS and direct digital control that have the ability to collect large amounts of data. However, even with modern technologies, those buildings are unfortunately still not operating optimally due to the absence of computational means and centralized solutions embedded into the BAS. Therefore, there is a significant need to investigate how modern computational techniques can help generate the analysis needed to gain full benefit from real-time data and at the same time perform many potential intelligent applications such as modelling, optimization, energy efficiency and energy assessment, and fault detection and diagnosis. This paper discusses the modeling methodologies for building energy system using time series auto regression artificial neural networks. The model can be integrated into energy solution tools for building energy assessment, optimization, fault detection and diagnosis, and many other applications. The model predicts whole building energy consumptions as function of four input variables, dry bulb and wet bulb outdoor air temperatures, hour of day and type of day. To train and test the models, data from twenty existing buildings and from simulations are used for the analysis. Advanced computational methods are used for data analysis and preprocessing. The wavelet basis is used to remove the noise and anomalies. Different neural network structures are tested along with various input delays to determine the one yielding the best results in term of mean square error. The results show that the developed model can provide results sufficiently accurate for its use in various energy efficiency and saving estimation applications.

Due to energy becoming a prominent topic in the sciences, creating baseline energy models for buildings has become a major area of research. Baseline models help determine dependency on ambient temperature or other parameters, while also helping show if energy reduction is due to building retrofits, occupancy, or ambient temperature. Several different methods of creating a baseline models for commercial and residential buildings, however few attempts have been made to create baseline energy models in industrial facilities, with only simple methods being analyzed. Since industrial facilities account for 33% of the annual energy usage within the United States (EIA), energy usage and finding energy saving opportunities in industrial facilities needs to be analyzed. While most industrial facilities energy usage is strongly dependent on production, some can be very dependent on weather especially if the facility is being conditioned. Even in an industrial facilities major energy consumption is used for production the energy usage might still be temperature dependent due to equipment being temperature dependent (i.e. chillers, furnaces, boilers, etc.). To understand an industrial facility’s energy usage is to create a baseline energy model. Currently only change-point regression models have been commonly used for analysis in industrial facilities. An analysis of the effectiveness of using Gaussian process regression (GPR) to develop a baseline energy usage models in industrial facilities from utility bill data and hourly data is presented in this paper.  Gaussian process regression uses a covariance matrix of the input variables to construct the model compared to using a pre-defined relationship between the input-output variables. By using a covariance matrix Gaussian process regression is more flexible than traditional parametric regression models. Two case studies are presented: using utility bill data to create a Gaussian process regression model and a using hourly data to create a Gaussian process regression model. In all cases the baseline regression models gave a CV-RMSE of 20% or lower showing that hourly data or utility bill data is capable of producing accurate baseline models defined by ASHRAE Guideline 14.