Detailed occupancy prediction, occupancy-sensing control and advanced behavioural modelling within whole-building energy simulation

Cette étude a pour but de combler l'écart entre l'état actuel de la simulation énergétique dans le domaine du bâtiment (i.e. hypothèses et modèles) et la connaissance empirique sur le comportement des usagers en matière de contrôle environnemental. L'application principale issue de cette thèse est un module de simulation autonome qui vise la modélisation à haute résolution et à haute fréquence des interactions personne-milieu: de l'occupation des locaux (i.e. l'affectation individuelle d'un environnement modélisé), du contrôle basé uniquement sur la présence ou l'absence des occupants (e.g. détecteurs de mouvement), jusqu'aux modèles comportementaux plus avancés (e.g. commutation manuelle des appareils d'éclairage, l'utilisation des fenêtres ouvrantes). L'intégration du module au sein du logiciel libre ESP-r, un programme qui permet de simuler l'ensemble des interactions bâtiment-systèmes-environnement, permet d'étudier à quel point les modèles d'interactions personne-milieu, issus des études en milieu réel, peuvent influencer les besoins énergétiques d'un bâtiment donné. Certains traits comportementaux, couramment associés aux modèles de contrôle manuel des systèmes d'éclairage, caractérisent également le comportement individuel au niveau des fenêtres ouvrantes; une conclusion issue d'une étude pilote en milieu réel sur le campus de l'Université Laval (Québec). Cette constatation suggère certains traits communs pouvant décrire le comportement des usagers en matière de contrôle environnemental. Le module développé permet également d'étudier le potentiel écoénergétique de stratégies innovatrices: l'application de stratégies de contrôle reposant sur l'adaptation thermique dans un contexte de climatisation hybride, et basées sur l'opération de fenêtres ouvrantes en tant que commutateurs entre climat naturel et climat artificiel. Les résultats préliminaires suggèrent que pour les climats nordiques ou méridionaux, ces approches permettent effectivement de réduire les besoins en climatisation, mais qu'en contre partie les besoins en chauffage augmentent considérablement en raison de l'utilisation des fenêtres en périodes plus tempérées. L'intérêt de la méthode est ici mis en évidence dans sa capacité à simuler globalement l'ensemble des conséquences énergétiques de l'interaction sociale avec l'environnement bâti.This study sets out to bridge the gap between building energy simulation and empirical evidence on occupant behaviour. The major output is a self-contained simulation module that aims to control all occupant-related phenomena which can affect energy use in buildings. It provides high resolution and high frequency occupancy prediction (i.e. when occupants as individual agents occupy a modelled environment), occupant-sensing control (i.e. as driven by the mere presence of one or more occupants, such as occupancy-sensing lighting controls), as well as advanced behavioural models (i.e. active personal control, such as manual switching of lights, manual adjustments to window blinds, operable windows, personalized air-conditioning units). The module is integrated within the ESP-r free software, a whole-building energy simulation program. Simulation results clearly show that occupants-based phenomena exert a strong influence on simulated energy use, revealing a number of limitations in key assumptions in current energy simulation practice. Key behavioural traits, commonly associated to lighting behavioural patterns, also appear to be associated to personal control of operable windows, as demonstrated in a pilot field study in a Université Laval pavilion in Québec. This may suggest an abstract quality to certain behavioural concepts regarding different environmental controls. The study then focuses on the use of the developed work to investigate the energy saving potential of novel yet untried strategies: adaptive comfort control algorithms in hybrid environments, based on the use of operable windows as switching mechanisms between natural and artificial modes of environmental control. Results suggest that for both heating- and cooling-dominant climates, adaptive comfort control effectively reduces cooling requirements, yet operable window use during cooler conditions appear to increase heating requirements. The usefulness of the original method is here illustrated by providing a more complete view on energy use attributed to occupant behaviour

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

A multidisciplinary research approach to energy-related behavior in buildings

Occupant behavior in buildings is one of the key drivers of building energy performance. Closing the “performance gap” in the building sector requires a deeper understanding and consideration of the “human factor” in energy usage. For Europe and US to meet their challenging 2020 and 2050 energy and GHG reduction goals, we need to harness the potential savings of human behavior in buildings, in addition to deployment of energy efficient technologies and energy policies for buildings. Through involvement in international projects such as IEA ECBC Annex 53 and EBC Annex 66, the research conducted in the context of this thesis provided significant contributions to understand occupants’ interactions with building systems and to reduce their energy use in residential and commercial buildings over the entire building life cycle. The primary goal of this Ph.D. study is to explore and highlight the human factor in energy use as a fundamental aspect influencing the energy performance of buildings and maximizing energy efficiency – to the same extent as technological innovation. Scientific literature was reviewed to understand state-of-the-art gaps and limitations of research in the field. Human energy-related behavior in buildings emerges a stochastic and highly complex problem, which cannot be solved by one discipline alone. Typically, a technological-social dichotomy pertains to the human factor in reducing energy use in buildings. Progressing past that, this research integrates occupant behavior in a multidisciplinary approach that combines insights from the technical, analytical and social dimension. This is achieved by combining building physics (occupant behavior simulation in building energy models to quantify impact on building performance) and data science (data mining, analytics, modeling and profiling of behavioral patterns in buildings) with behavioral theories (engaging occupants and motivating energy-saving occupant behaviors) to provide multidisciplinary, innovative insights on human-centered energy efficiency in buildings. The systematic interconnection of these three dimensions is adopted at different scales. The building system is observed at the residential and commercial level. Data is gathered, then analyzed, modeled, standardized and simulated from the zone to the building level, up to the district scale. Concerning occupant behavior, this research focuses on individual, group and collective actions. Various stakeholders can benefit from this Ph.D. dissertation results. Audience of the research includes energy modelers, architects, HVAC engineers, operators, owners, policymakers, building technology vendors, as well as simulation program designers, implementers and evaluators. The connection between these different levels, research foci and targeted audience is not linear among the three observed systems. Rather, the multidisciplinary research approach to energy-related behavior in buildings proposed by this Ph.D. study has been adopted to explore solutions that could overcome the limitations and shortcomings in the state-of-the-art research

Energy Forensics Analysis

The energy consumed by a building can reveal information about the occupants and their activities inside the building. This could be utilized by industries and law enforcement agencies for commercial or legal purposes. Utility data from Smart Meter (SM) readings can reveal detailed information that could be mapped to foretell resident occupancy and type of appliance usage over desired time intervals. However, obtaining SM data in the United States is laborious and subjected to legal and procedural constraints. This research develops a user-driven simulation tool with realistic data options and assumptions of potential human behavior to determine energy usage patterns over time without any utility data. In this work, factors such as occupant number, the possibility of place being occupied, thermostat settings, building envelope, appliances used in households, appliance capacities, and the possibility of using each appliance, weather, and heating-cooling systems specifications are considered. For five specific benchmarked scenarios, the range of the random numbers is specified based on assumed potential human behavior for occupancy and energy-consuming appliances usage possibility, with respect to the time of the day, weekday, and weekends. The simulation is developed using the Visual Basic Application (VBA)® in Microsoft Excel®, based on the discrete-event Monte Carlo Simulation (MCS). This simulation generates energy usage patterns and electricity and natural gas costs over 30-minutes intervals for one year. The simulated energy usage and the cost are reflected in the sensitivity analysis by comparing factors such as occupancy, appliance type, and time of the week. This work is intended to facilitate the analysis of building occupants\u27 activities by various stakeholders, subject to all legal provisions that apply. It is not intended for the general public to pursue these activities because legal ramifications might be involved

Automated demand response applied to a set of commercial buildings

Commercial facility demand response refers to voluntary actions by customers that change their consumption of electric power in response to price signals, incentives, or directions from grid operators at times of high wholesale market prices or when electric system reliability is jeopardized. Energy management in a commercial facility can be segregated into two areas: energy efficiency and demand response. This dissertation assesses both in two commercial facilities: one designed and constructed prior to the development of demand response principles and the second designed and constructed with modern energy controls and energy efficient materials. The energy evaluation identified opportunities for energy conservation and strategies for demand response. Next this paper presents a fuzzy method for predicting a facilitys baseline load profile. The baseline load profile is the predicted energy use of a facility during a demand response event in the absence of any energy reduction. During a demand response event, building operators or their automated control systems make adjustments to building operations with the goal of reducing the building\u27s electric load during times of the electric system\u27s peak electric usage. The baseline load profile is key to assessing the actual peak load electric energy reduction from a demand response event. Some grid operators are considering compensating commercial facilities for the energy reduction they achieve during demand response events. In fact the Public Service Company of New Mexico, the electricity supplier to UNM, has a demand response program that would compensate in this manner. The method described here is based on fuzzy set theory and allows the inclusion of building occupancy in the calculation. Our method achieves greater accuracy than other methods currently in use. Third, this study developed strategies for minimizing occupant dissatisfaction during demand response events using fuzzy cognitive mapping. If occupant discomfort causes significant complaints to the facility operator or owner, they may direct the demand response event be discontinued and thus eliminate the electric power savings. Assessing and predicting this potential interruption of the demand response event is not readily evaluated with crisp analytical techniques. Thus we elected to assess this problem using fuzzy set theory as applied to cognitive maps. Our model focuses on the University of New Mexico (UNM) campus. Fourth, we developed the conceptual design and operation of a facility control system to manage demand response events for the campus of the University of New Mexico. This section presents the design principles, the demand response control system logic and operation, and the economic value based on the PNM Peak Saver Demand Response Program financial incentives.\u2

Autonomic management of a building's multi-HVAC system start-up

Most studies about the control, automation, optimization and supervision of building HVAC systems concentrate on the steady-state regime, i.e., when the equipment is already working at its setpoints. The originality of the current work consists of proposing the optimization of building multi-HVAC systems from start-up until they reach the setpoint, making the transition to steady state-based strategies smooth. The proposed approach works on the transient regime of multi-HVAC systems optimizing contradictory objectives, such as the desired comfort and energy costs, based on the "Autonomic Cycle of Data Analysis Tasks" concept. In this case, the autonomic cycle is composed of two data analysis tasks: one for determining if the system is going towards the defined operational setpoint, and if that is not the case, another task for reconfiguring the operational mode of the multi-HVAC system to redirect it. The first task uses machine learning techniques to build detection and prediction models, and the second task defines a reconfiguration model using multiobjective evolutionary algorithms. This proposal is proven in a real case study that characterizes a particular multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is impressive since there is a high level of conformity for the multi-HVAC system to reach the setpoint and deliver the operation to the steady-state smoothly, avoiding overshooting and other non-desirable transitional effects.European CommissionJunta de Comunidades de Castilla-La ManchaMinisterio de Ciencia e Innovació

Demand Response in Smart Grids

The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer

How the italian residential sector could contribute to load flexibility in demand response activities: a methodology for residential clustering and developing a flexibility strategy

This work aims at exploring the potential contribution of the Italian residential sector in implementing load flexibility for Demand Response activities. In detail, by combining experimental and statistical approaches, a method to estimate the load profile of a dwelling cluster of 751 units has been presented. To do so, 14 dwelling archetypes have been defined and the algorithm to categorise the sample units has been built. Then, once the potential flexible loads for each archetype have been evaluated, a control strategy for applying load time shifting has been implemented. That strategy accounts for both the power demand profile and the hourly electricity price. Specifically, it has been assumed that end users access a pricing mechanism following the hourly trend of electricity economic value, which is traded day by day in the Italian spot market, instead of the current Time of Use (TOU) system. In such a way, it is possible to flatten the dwellings cluster profile, limiting undesired and unexpected results on the balancing market. In the end, monthly and yearly flexibility indexes have been defined along with the strategy effectiveness parameter. From calculations, it emerges that a dwelling cluster for the Italian residential sector is characterised by a flexibility index of 10.3% and by a strategy effectiveness equal to 34%. It is noteworthy that the highest values for flexibility purpose have been registered over the heating season (winter) for the weekends