PREDICTING HVAC ENERGY CONSUMPTION IN COMMERCIAL BUILDINGS USING MULTIAGENT SYSTEMS

ABSTRACT Energy consumption in commercial buildings has been increasing rapidly in the past decade. The knowledge of future energy consumption can bring significant value to commercial building energy management. For example, prediction of energy consumption decomposition helps analyze the energy consumption patterns and efficiencies as well as waste, and identify the prime targets for energy conservation. Moreover, prediction of temporal energy consumption enables building managers to plan out the energy usage over time, shift energy usage to off-peak periods, and make more effective energy purchase plans. This paper proposes a novel model for predicting heating, ventilation and air conditioning (HVAC) energy consumption in commercial buildings. The model simulates energy behaviors of HVAC systems in commercial buildings, and interacts with a multiagent systems (MAS) based framework for energy consumption prediction. Prediction is done on a daily, weekly and monthly basis. Ground truth energy consumption data is collected from a test bed office building over 267 consecutive days, and is compared to predicted energy consumption for the same period. Results show that the prediction can match 92.6 to 98.2% of total HVAC energy consumption with coefficient of variation of the root mean square error (CV-RMSE) values of 7.8 to 22.2%. Ventilation energy consumption can be predicted at high accuracies (over 99%) and low variations (CV-RMSE values of 3.1 to 16.3%), while cooling energy consumption accounts for majority of inaccuracies and variations in total energy consumption prediction

Coordinating occupant behavior for building energy and comfort management using multi-agent systems

Issue for Planning Future Cities-Selected papers from the 2010 eCAADe Conference</p

Toward adaptive comfort management in office buildings using participatory sensing for end user driven control

Current building management systems (BMS) operate based on conservatively defined operational hours, maximum occupancy rates, and standardized occupant comfort set points. Despite the increasing building energy consumption rates, occupants are not usually satisfied with the indoor conditions in commercial buildings. This study proposes an intermediary communication platform, which enables occupants to communicate their preferences to the BMS. The objective is to facilitate the communication between humans and buildings toward adaptive end user comfort management and to compensate for high rate of discomfort in office buildings. The design process of the intermediary, as well as the participatory sensing approach for deploying it in a test bed is presented. The key element is the interpretation of occupants ’ preferences in the form of change in the HVAC system operations. The results are presented to investigate the correlation between sensed ambient conditions and the user preferences. The results show that although there is a weak to moderate correlation between ambient temperature, humidity, and occupants’ preferences, the variation of correlation for different occupants is relatively high

Energy trade off analysis of optimized daily temperature setpoints

We introduce a systematic approach for analyzing the energy consumption of four control policies (i.e., zone level daily optimal control, zone level annual optimal control, building level daily optimal control, building level annual optimal control), which differed based on their temporal and spatial control scales. In order to integrate occupant thermal comfort requirements, we defined uniformly distributed random constraint functions on the setpoints. We used the DOE reference small office building in three U.S. climate zones for simulating the performances of control policies, using EnergyPlus. Among the four control policies, the building level annual control policy showed close to the highest energy efficiency (27.76% to 50.91% (average of 39.81%) savings depending on the climate) with comparatively small training data requirements. In addition, the building level daily optimal setpoint selection, subject to thermal comfort constraints, leads to 17.64 – 38.37% (average of 26.61%) energy savings depending on the climate. We also demonstrate that temporal scale of the policies have a statistically significant impact on the small office building’s energy consumption while spatial scale’s impact is insignificant

An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling

HVAC systems are the major energy consumers in commercial buildings in the United States. These systems are operated to provide comfortable thermal conditions for building occupants. The common practice of defining operational settings for HVAC systems is to use fixed set points, which assume occupants have static comfort requirements. However, thermal comfort has been shown to vary from person to person and also change over time due to climatic variations or acclimation. In this paper, we introduce an online learning approach for modeling and quantifying personalized thermal comfort. In this approach, we fit a probability distribution to each comfort condition (i.e., uncomfortably warm, comfortable, and uncomfortably cool) data set and define the overall comfort of an individual through combing these distributions in a Bayesian network. In order to identify comfort variations over time, Kolmogorov–Smirnov test is used on the joint probability distributions. In order to identify comfortable environmental conditions, a Bayesian optimal classifier is trained using online learning. In order to validate the approach, we collected data from 33 subjects, and an average accuracy of 70.14% and specificity of 76.74% were achieved. In practice, this approach could transform the comfort objectives to constrain functions and prevents pareto optimality problems

Energy trade off analysis of optimized daily temperature setpoints

We introduce a systematic approach for analyzing the energy consumption of four control policies (i.e., zone level daily optimal control, zone level annual optimal control, building level daily optimal control, building level annual optimal control), which differed based on their temporal and spatial control scales. In order to integrate occupant thermal comfort requirements, we defined uniformly distributed random constraint functions on the setpoints. We used the DOE reference small office building in three U.S. climate zones for simulating the performances of control policies, using EnergyPlus. Among the four control policies, the building level annual control policy showed close to the highest energy efficiency (27.76% to 50.91% (average of 39.81%) savings depending on the climate) with comparatively small training data requirements. In addition, the building level daily optimal setpoint selection, subject to thermal comfort constraints, leads to 17.64 – 38.37% (average of 26.61%) energy savings depending on the climate. We also demonstrate that temporal scale of the policies have a statistically significant impact on the small office building’s energy consumption while spatial scale’s impact is insignificant