ASHRAE OR-16-C078-2016 Bayesian Network Based HVAC Energy Consumption Prediction Using Improved Fourier series Decomposition.pdf
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1、 Fuxin Niu is a PhD student and Zheng ONeill is an assistant professor in the Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL, USA. Bayesian Network Based HVAC Energy Consumption Prediction Using Improved Fourier series Decomposition Fuxin Niu, PhD Zheng ONeill, PhD,
2、PE Student Member ASHRAE Member ASHRAE ABSTRACT An accepted Heating, Ventilation and Air-conditioning (HVAC) energy consumption model is a necessary step for various applications including fault detection and diagnostics, measurement and verification in building retrofit. It is common to use a polyn
3、omial regression to decouple the baseload from total building energy consumption while considering the baseload as a fixed value. To improve the decoupling algorithm, Fourier series is introduced to represent the dynamic baseload. Furthermore, a probabilistic graphical Bayesian network model with di
4、screte and continuous variables is developed to predict the HVAC energy consumption. Sub-metering data from a four-story university dormitory is used to test the proposed Fourier series based decomposition and Bayesian Network based predictions. The results indicate that polynomial regression integr
5、ated Fourier series decomposition method is feasible and has a more accurate performance. Using the decomposed data, the HVAC system electricity energy consumption is predicted using a Bayesian network. The preliminary results suggested that the Bayesian network is a time-saving and accurate predict
6、ion model based on the ASHRAE Guideline 14 recommended metrics. INTRODUCTION Accurate energy performance prediction of Heating, Ventilation and Air-Conditioning (HVAC) system plays a significant role for intelligent building operations to improve energy efficiency and reduce energy consumption in bu
7、ildings. In modern commercial and residential buildings, large amounts of raw data, including electric metering data, are monitored, trended and saved in, for example, Building Automation System (BAS). Due to the complexity of building mechanical and electrical system and the cost, practically speak
8、ing, it is impossible to have sensors/meters to monitor the building at a fine granularity. Building total energy consumption (e.g., total electricity consumption) is one of the most commonly available metering data. How to decompose the total building energy consumption for an accurate estimation o
9、f the HVAC system energy consumption is important to analyze and manage HVAC system performance and operations. There are many approaches for baseline building energy estimation. ASHRAE Guideline 14 (ASHRAE 2002) and Internal Performance Measurement and Verification Protocol (IPMVP) (EVO 2012) provi
10、de rigorous approaches to develop baseline models for estimating energy savings due to retrofits. In practice, linear regression method is a common approach for building energy performance analysis. In a change-point model, the building performance is partitioned into different operating conditions
11、and a linear model is fit to each of the operating modes (Kissock et al. 2002). Both linear regression and Gaussian Process regression were used to develop an inverse model for a commercial building case (Zhang et al. 2013). Multi-variate linear model was developed to estimate a variable air volume
12、(VAV) energy saving potential after the system retrofit (Katipamula et al. 1993). Zhang et al. (2015) reviewed four mainstream baseline data-driven energy models used to characterize building energy performance: change-point regression model, Gaussian process regression (GPR) model, Gaussian mixture
13、 regression (GMR) model, and artificial neural network model. These models were then applied to an office building to predict the HVAC hot water energy consumption. The change-point method is the most appropriate for this case study in terms of accuracy vs. efforts spent for the modeling. This is ac
14、tually well aligned current practice in building measurement and verification industry. The occupant schedules and behavior certainly will have significant impacts on the model prediction accuracy for some cases (Clevenge et al. 2006). These factors could be easily adapted into the current framework
15、. Prediction of building HVAC system energy usage and its associated uncertainty analysis are critical to characterize the building baseline performance for impact assessments of energy saving strategies such as fault detection and diagnosis (FDD), control policies and retrofits. Srivastav et al. (2
16、013) presented a data-driven approach based on GMR for modeling building energy use with locally adaptive uncertainty quantification. GMR approach was found to be comparable to the polynomial model in terms of the accuracy of building energy consumption predictions. The predictive quality of the GPR
17、 model is strongly influenced by the range covered by the training and testing data set. ONeill (2014) presented a data driven probabilistic graphic model to predict building HVAC hot water energy consumption. A directed graphical model namely, a Bayesian Network (BN) model was created for such a pu
18、rpose. Each node in the BN represents a random variable and the links between the nodes are probabilistic dependencies among these corresponding variables. These dependencies are statistically learnt and/or estimated by using measured data and augmented by domain expert knowledge. The prediction res
19、ult by BN indicated that it was acceptable prediction method while providing more information such as uncertainty associated with predictions for risk management. Niu et al. (2015) analyzed air conditioning unit energy consumption prediction from different algorithms including BN, AutoRegresive with
20、 eXternal inputs, State Space and Subspace state space models. The results indicated that BN method has the most accurate estimation. In buildings, the baseload is the energy consumption irrelative with outdoor conditions such as outdoor air temperature. In general, it includes plug, lighting, refri
21、geration load, etc. (Ge and Tassou 2011). In this paper, in order to enhance the accuracy of building baseload estimation, an improved Fourier series decomposition based on linear regression method is proposed to estimate the total building energy consumption, corresponding baseload energy consumpti
22、on and HVAC system energy consumption. And then discrete and continuous variables BN models are applied to estimate HVAC system energy consumption based on the decoupled results from Fourier series decomposition. Finally, the comparison analysis of two BN models is conducted. TECHNICAL APPROACH In t
23、his case, there are two steps to estimate the HVAC energy consumption using BN method. Firstly, an improved Fourier series decomposition method is proposed to decouple the baseload from measured total building energy consumption. And then, HVAC system energy consumption will be predicted using BN mo
24、dels. Improved Fourier series Decomposition Currently, the polynomial regression method is widely used (Spyrou et al. 2015). It is assumed that total building energy consumption is only dependent on outdoor air temperature as shown in Equation (1). 230 1 2 3y d d T d T d T (1) Where d0 is the baselo
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