Development of Economic Water Usage Sensor and Cyber-Physical Systems Co-Simulation Platform for Home Energy Saving

In this thesis, two Cyber-Physical Systems (CPS) approaches were considered to reduce residential building energy consumption. First, a flow sensor was developed for residential gas and electric storage water heaters. The sensor utilizes unique temperature changes of tank inlet and outlet pipes upon water draw to provide occupant hot water usage. Post processing of measured pipe temperature data was able to detect water draw events. Conservation of energy was applied to heater pipes to determine relative internal water flow rate based on transient temperature measurements. Correlations between calculated flow and actual flow were significant at a 95% confidence level. Using this methodology, a CPS water heater controller can activate existing residential storage water heaters according to occupant hot water demand. The second CPS approach integrated an open-source building simulation tool, EnergyPlus, into a CPS simulation platform developed by the National Institute of Standards and Technology (NIST). The NIST platform utilizes the High Level Architecture (HLA) co-simulation protocol for logical timing control and data communication. By modifying existing EnergyPlus co-simulation capabilities, NIST’s open-source platform was able to execute an uninterrupted simulation between a residential house in EnergyPlus and an externally connected thermostat controller. The developed EnergyPlus wrapper for HLA co-simulation can allow active replacement of traditional real-time data collection for building CPS development. As such, occupant sensors and simple home CPS product can allow greater residential participation in energy saving practices, saving up to 33% on home energy consumption nationally

ANOMALY INFERENCE BASED ON HETEROGENEOUS DATA SOURCES IN AN ELECTRICAL DISTRIBUTION SYSTEM

Harnessing the heterogeneous data sets would improve system observability. While the current metering infrastructure in distribution network has been utilized for the operational purpose to tackle abnormal events, such as weather-related disturbance, the new normal we face today can be at a greater magnitude. Strengthening the inter-dependencies as well as incorporating new crowd-sourced information can enhance operational aspects such as system reconfigurability under extreme conditions. Such resilience is crucial to the recovery of any catastrophic events. In this dissertation, it is focused on the anomaly of potential foul play within an electrical distribution system, both primary and secondary networks as well as its potential to relate to other feeders from other utilities. The distributed generation has been part of the smart grid mission, the addition can be prone to electronic manipulation. This dissertation provides a comprehensive establishment in the emerging platform where the computing resources have been ubiquitous in the electrical distribution network. The topics covered in this thesis is wide-ranging where the anomaly inference includes load modeling and profile enhancement from other sources to infer of topological changes in the primary distribution network. While metering infrastructure has been the technological deployment to enable remote-controlled capability on the dis-connectors, this scholarly contribution represents the critical knowledge of new paradigm to address security-related issues, such as, irregularity (tampering by individuals) as well as potential malware (a large-scale form) that can massively manipulate the existing network control variables, resulting into large impact to the power grid

Cost effective and Non-intrusive occupancy detection in residential building through machine learning algorithm

Residential and commercial buildings consume more than 40% of energy and 76% of electricity in the U.S. Buildings also emit more than one-third of U.S. greenhouse gas emissions, which is the largest sector. A significant portion of the energy is wasted by unnecessary operations on heating, ventilation, and air conditioning (HVAC) systems, such as overheating/overcooling or operation without occupants. Wasteful behaviors consume twice the amount of energy compared to energy-conscious behaviors. Many commercial buildings utilize a building management system (BMS) and occupancy sensors to better control and monitor the HVAC and lighting system based on occupancy information. However, the complicated installation process of occupancy sensors and their long payback period have prevented consumers from adopting this technology in the residential sector. Hence, I explored a method to detect the presence of an occupant and utilize it to reduce energy wasting in residential buildings. Existing methods of occupancy detection often focus on directly measure occupancy information from environmental sensors. The validity of such a sensor network highly depends on the room configurations, so the approach is not readily transferrable to other residential buildings. Instead of direct measurement, the proposed scheme detects the change of occupancy in a building. The new scheme implements machine learning methods based on a sequence of human activities that happens in a short period. Since human activities are similar regardless of house floorplan, such an approach may lead to readily transferrable to other residential buildings. I explored three types of human activity sensor to detect door handle touch, water usage, and motion near the entrance, which are highly correlated with the change of occupancy. The occupancy change is not only based on one single human activity, it also depends on a series of human activities that happen in a short period, called event. As the events have different durations and cannot be readily applicable to existing machine learning models due to varying input matrix sizes. Hence, I devised a fixed format to summarize the event regardless of the total duration of the event. Then I used a machine learning model to identify the occupancy change based on the event data. The saving potential of occupancy driven thermostat is about 20 % of energy in residential buildings. However, the actual saving impact in any given house can vary significantly from the average value due to the large variety of residential buildings. Existing building simulation tools did not readily consider the random nature of occupancy and users’ comfort. For this reason, I explored a co-simulation platform that integrates an occupancy simulator, a cooling/heating setpoint control algorithm, a comfort level evaluator, and a building simulator together. I explored the annual energy saving impact of an occupancy-driven thermostat compare with a conventional thermostat. The simulation had been repeated in five U.S. cities (Fairbanks, New York City, San Francisco, Miami, and Phoenix) with distinctive climate zones

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

Consumption in Different European Countries

Humanity consumes more natural resources than the Earth has the ability to regenerate them. Given this problem, world eyes are over sustainability. Several actions for reducing and consume natural resources efficiently are being carried out worldwide. The European Union has developed several strategies for the efficient use of resources, including energy. There is a high potential for energy savings in the building stock sector, mainly in the residential buildings area. This study focused on the single-family houses to know the factors that affect the energy consumption through a comparative analysis of the energy used in dwellings of different European Mediterranean countries. The countries chosen for this analysis were Greece, Portugal, and Spain, countries that share similar geographical, political and meteorological conditions. For this analysis, only was taken into account the energy used for space heating and cooling and water heating. Using statistical data and national regulations, two regions of each country were chosen, and a typical house from each region was represented to obtain annual energy consumption results through simulation. Variable parameters such as internal temperatures, system operation hours and occupant behavior were tested on the models to see their impacts on energy consumption. As a result of simulations, it was concluded that space heating is the activity that consumes the most among the selected ones, therefore, having a great potential for energy savings. The efficient use of energy keeps the thermal comfort of the occupants reducing the energy consumption. The study showed that several factors affect energy consumption, but the main one is human behavior. The role of energy policies are important over the consumption through people´s behavior. The evolution of the policies in Europe has been remarkable in recent years, but they are not enough. Tighter policies that act fast enough are necessary. Occupants must balance the consumption between comfort and energy awareness, aiming to shift some of the multiples small decisions and actions taken every day, in order to create a more sustainable future on our planet

Exploration in-situ et numérique de la consommation énergétique et du confort thermique des bâtiments résidentiels en bois

Plus du tiers de l’énergie consommée et des émissions de gaz à effet de serre dans l’atmosphère sont causées par le secteur du bâtiment. Ce dernier joue ainsi un grand rôle dans la lutte au réchauffement climatique et il est impératif d’améliorer son efficacité énergétique, ce qui demande une excellente compréhension du comportement thermique des bâtiments. Les outils de simulation énergétique de bâtiments sont fort utiles à cet effet, mais il y a malheureusement souvent des écarts observés entre la consommation réelle d’un bâtiment et ce qui était attendu. Étant un aspect fort probabiliste de l’opération d’un bâtiment, le comportement des occupants est difficile à représenter fidèlement lors des simulations de bâtiments. Or, vu le grand impact que les occupants ont sur la performance d’un bâtiment, il est essentiel d’avoir une représentation viable de cet aspect de la simulation. L’objectif de cette thèse est d’analyser les dessous de la consommation énergétique des bâtiments résidentiels en bois à haute performance énergétique en se concentrant principalement sur le rôle joué par les occupants. Cette thèse se base sur le suivi détaillé d’un bâtiment de logements sociaux présentement en opération. Des pistes de solutions sont proposées dans le but d’améliorer davantage la performance des bâtiments à faible consommation énergétique. Dans un premier temps, la consommation énergétique du bâtiment étudié est analysée de fonds en comble afin de comprendre pourquoi le bâtiment a besoin d’énergie. Cette évaluation expose de grandes variations de consommation énergétique et de confort thermique entre les logements. Cette grande variabilité n’est pas explicable ni par les différentes orientations et position des logements, ni par le nombre d’occupants dans les logements; les données montrent le grand effet que les gens peuvent avoir sur la performance de leur logement par les gestes qu’ils posent. Des modèles de régression linéaire sont formés à partir des données mesurées et quantifient l’impact de différentes variables sur la demande en chauffage en hiver et sur la température intérieure des logements en été. La température intérieure du bâtiment est un enjeu important puisque de la surchauffe est présente durant la saison estivale. La forte isolation et la grande étanchéité de l’enveloppe du bâtiment contribue à cette surchauffe en empêchant les transferts thermiques entre les environnements intérieur et extérieur. L’écart de performance énergétique du bâtiment étudié est également abordé. Il est montré que pour cette étude de cas, l’écart est principalement par une mauvaise représentation du comportement des occupants dans le modèle numérique du bâtiment. Un modèle stochastique simulant le comportement des occupants dans les bâtiments résidentiels est développé à partir de modèles déjà existant. Cet outil simule à la fois la présence des occupants dans leur logement, leur consommation d’eau chaude et d’électricité, ainsi que leur comportement vis-à-vis le contrôle des fenêtres. Les profils générés sont cohérents entre eux (il ne peut pas y avoir de consommation d’eau chaude si personne n’est présent) et considèrent la diversité inter-ménage du comportement des occupants. La portion traitant du contrôle des fenêtres est construite à partir des données mesurées au bâtiment étudié alors que ces données ont plutôt servies à guise de validation pour les autres parties du modèle. Cette validation montre les bienfaits des modifications apportés aux modèles déjà existants. Des simulations sont par la suite effectuées pour quantifier l’impact des occupants sur la performance énergétique des bâtiments résidentiels. Ces simulations se basent sur l’outil stochastique du comportement des occupants développé durant cette thèse. Les résultats montrent que la demande en chauffage d’un logement, sa consommation totale d’énergie et son confort thermique sont très sensible aux gestes posés par les occupants. Un modèle de régression linéaire est également construit à partir des résultats de simulation pour mesurer l’influence des divers paramètres. Un bâtiment à plusieurs unités logements est moins robuste au comportement des occupants qu’une maison unifamiliale, mais les résultats suggèrent qu’il demeure difficile de prévoir avec exactitude la performance d’un bâtiment multirésidentiel si l’aspect stochastique du comportement des occupants est négligée. L’utilisation de profils plus précis du comportement des occupants peut aussi améliorer le dimensionnement des systèmes mécaniques, notamment les systèmes d’eau chaude.Over a third of energy use and greenhouse gas emissions are related to the building sector. As part of global efforts to combat climate change, it is essential to ensure high energy efficiency of buildings. Doing so requires a deep understanding of the thermal behavior of buildings. Building performance simulation is very useful in this regard, but it is frequent to observe discrepancies between the predicted and real energy consumption levels. Occupant behavior is very influential on the energy performance of a building, so it is essential for it to be accurately represented during building simulations. The objective of this thesis is to analyze and explain the consumption of energy in high-performance wood residential buildings by focusing on the importance of occupant behavior. This thesis relies on the monitoring of a social housing building. Potential solutions are proposed to further improve the performance of low energy consumption buildings. First, the energy consumption of the monitored building is studied in order to understand why the building requires energy. This analysis exhibits the great dwelling-to-dwelling variability of energy consumption and thermal comfort. This variability is not explainable by the various orientations and positions of the dwellings or by the different household sizes. This shows the great impact that actions taken by people at home can have on the performance of their dwelling. Linear regression models are created from the collected data to quantify the influence of multiple variables on the heating demand in winter and on the indoor temperature in summer. Indoor temperature represents an important issue since overheating is present in the building during the summer. The high insulation and air tightness of the building envelope contributes to overheating by preventing heat transfer between the indoor and outdoor environments. The energy performance gap of the building is also covered. It is demonstrated that for the case study building, the gap is mainly due to an inaccurate representation of occupant behavior during building simulations. A stochastic model that simulates occupant behavior in residential buildings is developed from already existing models. This tool simultaneously simulates occupancy, hot water and electricity consumption and window control behavior. Generated profiles are coherent with each other (there cannot be hot water consumption when no one is present at home) and consider the dwelling-to-dwelling variability of occupant behavior. The window control part of the model is built from the data coming from the monitored building whereas the data is instead use to validate the other parts of the model. The validation shows the benefits of the modifications brought to the original occupant behavior models. Building simulations are then performed to assess the impact of occupants on the energy consumption and thermal comfort of residential buildings. These simulations are based on the stochastic occupant behavior tool develop in this thesis. Results display that the heating demand of a dwelling, its total energy use and its thermal comfort are all highly sensitive to occupant behavior. A linear regression model is also built from simulated data to evaluate the influence of various parameters. The energy performance of large housing stocks is more robust with respect to occupant behavior, but the results suggest that it remains difficult to forecast with great accuracy the performance of a multiresidential building if stochastic aspects of occupant behavior are neglected. Use of more accurate occupant behavior profiles can also improve the sizing of HVAC systems, particularly of hot water systems

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Modelling and Predicting Energy Usage from Smart Meter Data and Consumer behaviours in Residential Houses.

Efforts of electrical utilities to respond to climate change requires the development of increasingly sophisticated, integrated electrical grids referred to as the smart grids. Much of the smart grid effort focuses on the integration of renewable generation into the electricity grid and on increased monitoring and automation of electrical transmission functions. However, a key component of smart grid development is the introduction of the smart electrical meter for all residential electrical customers. Smart meter deployment is the corner stone of the smart grid. In addition to adding new functionality to support system reliability, smart meters provide the technological means for utilities to institute new programs to allow their customers to better manage and reduce their electricity use and to support increased renewable generation to reduce greenhouse emissions from electricity use. As such, this thesis presents our research towards the study of how the data (energy usage profiles) produced by the smart meters within the smart grid system of residential homes is used to profile energy usage in homes and detect users with high fuel consumption levels. This project concerns the use of advanced machine learning algorithms to model and predict household behaviour patterns from smart meter readings. The aim is to learn and understand the behavioural trends in homes (as demonstrated in chapter 5). The thesis shows the trends of how energy is used in residential homes. By obtaining these behavioural trends, it is possible for utility companies to come up with incentives that can be beneficial to home users on changes that can be adopted to reduce their carbon emissions. For example consumers would be more likely prompted to turn of unusable appliances that are consuming high energy around the home e.g., lighting in rooms which are un occupied. The data used for the research is constructed from a digital simulation model of a smart home environment comprised of 5 residential houses. The model can capture data from this simulated network of houses, hence providing an abundance set of information for utility companies and data scientist to promote reductions in energy usage. The simulation model produces volumes of outliers such as high periods (peak hours) of energy usage and low periods (Off peak hours) of anomalous energy consumption within the residential setting of five homes. To achieve this, performance characteristics on a dataset comprised of wealthy data readings from 5 homes is analysed using Area under ROC Curve (AUC), Precision, F1 score, Accuracy and Recall. The highest result is achieved using the Two-Class Decision Forest classifier, which achieved 87.6% AUC

Implementing Productivity Based Demand Response in Office Buildings Using Building Automation Standards

Demand response is an effective method that can solve known issues in electrical power systems caused by peak power demand and intermittent supply from renewable sources. Office buildings are good candidates for implementing demand response because they usually incorporate building management systems which are able to control and monitor various electrical devices, from lighting to HVAC, security to power management. In order to study the feasibility of using an existing office building management system to implement demand response, a simulator for a typical office building has been built which models the energy consumption characteristics of the building. With the help of this simulator, an Indoor Environment Quality based control algorithm is developed whose aim is to minimise reduction in productivity in an office building during a demand response application. This research revealed two key elements of automatic demand response: lighting loads need to be utilised in every demand response scenario along with HVAC, and the control system needs to be able to operate rapidly because of changing conditions. A multi-agent based demand response control algorithm for lighting is then developed and used to test the suitability of two communication protocols currently widely used in office buildings: KNX and LonWorks. The results show that excessive overload of the communication channel and the lag caused by slow communication speeds using these protocols present serious problems for the implementation of real time agent based communication in office buildings. A solution to these problems is proposed