Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

A Cognitive Social IoT Approach for Smart Energy Management in a Real Environment

Energy usage inside buildings is a critical problem, especially considering high loads such as Heating, Ventilation and Air Conditioning (HVAC) systems: around 50% of the buildings’ energy demand resides in HVAC usage which causes a significant waste of energy resources due to improper uses. Usage awareness and efficient management have the potential to reduce related costs. However, strict saving policies may contrast with users’ comfort. In this sense, this paper proposes a multi-user multi-room smart energy management approach where a trade-off between the energy cost and the users’ thermal comfort is achieved. The proposed user-centric approach takes advantage of the novel paradigm of the Social Internet of Things to leverage a social consciousness and allow automated interactions between objects. Accordingly, the system automatically obtains the thermal profiles of both rooms and users. All these profiles are continuously updated based on the system experience and are then analysed through an optimization model to drive the selection of the most appropriate working times for HVACs. Experimental results in a real environment demonstrated the cognitive behaviour of the system which can adapt to users’ needs and ensure an acceptable comfort level while at the same time reducing energy costs compared to traditional usage

Modélisation du comportement humain réactif et délibératif avec une approche multi-agent pour la gestion énergétique dans le bâtiment

Energy consumption in buildings is affected by various factors including its physical characteristics, the appliances inside, and the outdoor environment, etc. However, inhabitants’ behaviour that determines the global energy consumption must not be forgotten. In most of the previous works and simulation tools, human behaviour is modelled as occupancy profiles. In this thesis the focus is more on detailed behaviour representation, particularly the cognitive, reactive, and deliberative mechanisms. The inhabitants’ dynamic behaviour is modelled and co-simulated together with the physical aspects of a building and an energy management system. The analysis of different household appliances has revealed that energy consumption patterns are highly associated with inhabitants’ behaviours. Data analysis of inhabitants’ actions and appliances’ consumptions is used to derive a model of inhabitants’ behaviour that impacts the energy consumption. This model represents the cognitive mechanisms that provide causes that motivate the actions, including the communication with other inhabitants. An approach based on multi-agent systems is developed along with a methodology for parameter tuning in the proposed behaviour model. These tools are used to co-simulate, not only the physical characteristics of the building, the reactive behaviour that is sensitive to physical data, and deliberative behaviour of the inhabitants, but also the building energy management system. The energy management system allows the direct adjustment of the building parameters or simply giving advice to the inhabitants. The impact of different types of inhabitants’ behaviours, with and without the inclusion of an energy management system is analyzed. This work opens new perspectives not only in the building simulation and in the validation of energy management systems but also in the representation of buildings in the smart grid where signals can be sent to end users advising them to modulate their consumption.La consommation énergétique dans le secteur bâtiment dépend de diverses facteurs parmi lesquels ses caractéristiques physique, ses équipements, l’environnement extérieur, etc… mais il ne faut pas oublier le comportement des habitants qui est déterminant pour la consommation énergétique globale. Or, la plupart des travaux et outils représentent les occupants par des profils d’occupation. Cette thèse s’intéresse à la représentation plus détaillée du comportement des occupants, en particulier les mécanismes cognitifs, réactifs et délibératifs. Le comportement dynamique des occupants est modélisé et co-simulé avec les aspects physiques et des éventuels systèmes de gestion énergétique. L’analyse de la consommation de différents équipements électroménagers met en évidence que le consommation énergétique est très dépendante des comportements des occupants. L’analyse des consommations et des actions des habitants permet d’élaborer un modèle du comportement des occupants impactant la consommation énergétique. Le modèle représente des mécanismes cognitifs, qui représente les causes qui motivent les actions, incluant des échange avec d’autres acteurs humains. Une approche à base d’agents logiciels a été développée. Outre les aspects techniques, une méthodologie de réglage des paramètres des modèles de comportement est proposée. Ces outils sont utilisés pour réaliser une co-simulation représentant la physique du bâtiment, le comportement réactif, c’est-à-dire sensible aux données physiques, et délibératif des habitants mais aussi un système de gestion énergétique qui peut ajuster directement la configuration du logement ou simplement conseiller ces occupants. L’impact de différents types de comportements, avec et sans gestionnaire énergétique est analysé. Ces travaux ouvrent de nouvelles perspectives dans la simulation bâtiment, dans la validation de gestionnaires énergétiques mais aussi dans la représentation des bâtiments dans les réseaux d’énergie dits intelligents, dans lesquels des signaux peuvent être envoyés aux utilisateurs finaux pour les inviter à moduler leur consommation

A meta-synthesis review of occupant comfort assessment in buildings (2002-2022)

Occupant comfort in buildings is one of the most crucial considerations in designing a building. Accordingly, there is a growing interest in this area. Aspects of comfort include thermal comfort, visual comfort, acoustic comfort, and indoor air quality (IAQ) satisfaction. The objective of this state-of-the-art review was to provide a comprehensive, explicit, and up-to-date literature review on occupant comfort in buildings, since this issue has a great impact on the lifestyle, health, and productivity of occupants. A meta-synthesis method was also used for an analytical-interpretive review of previous studies. In this research, scientific research studies related to the subject of indoor occupant comfort in the period 2002–2022 were reviewed. Previous reviews have often covered the fundamental concepts and principles related to indoor occupant comfort. Although innumerable studies have focused on thermal comfort, other aspects of occupant comfort have not been considered. The review is analyzed and discussed in reference to type of study, case study geographical locations and climate zones, case study building types, decision-making models, assessment criteria, datacollection tools, and data analysis strategies. Finally, future research recommendations are presented. Through the review, we find that the comfort models used in research are mostly based on comfort perception votes collected from experimental studies, which may not reflect the preferences of users well. In addition, only the influence of environmental factors on the models has been investigated, and other personal factors have been ignored. This study presents a useful guide for researchers to determine their outlines for future research in this field

Design and control of mixed-mode cooling and ventilation in low-energy residential buildings in India

Energy security, climate change and economic growth are matters of critical international importance in an effort to achieve a sustainable future. Energy consumption in buildings contributes to higher greenhouse gas emissions than the industrial or transportation sectors combined. In India, the energy in the residential sector accounts for almost 50% of the total energy consumption. The need for comfortable internal environments, healthy indoor air quality and the consequences of global warming are all contributing factors to the high reliance on mechanical cooling and ventilation systems. In recent years, financial growth and increase in disposable income in India, have accelerated purchases of such mechanical systems. In metropolitan cities of India with extreme climates (hot and dry, warm and humid), the use of these systems increases by 30% every year. This upward trend is likely to continue in response to occupants’ higher comfort expectations and the continuous increase of the outside temperature during the summer months due to climate change. This could further impact the climate and the electricity grid. Innovative solutions should establish reliable strategies for cooling purposes by utilizing the use of natural ventilation. Mixed-mode buildings rely on both mechanical and natural systems to maintain comfortable conditions. Although the performance of mixed-mode buildings has already been studied and there is evidence for its positive impact on the reduction of energy demand, there is still a lack of knowledge on the best methods for controlling mixed-mode buildings. Today, the majority of the available algorithms for the control of mixed-mode systems are very simplistic and at a primitive stage of development. Typically, the control algorithms “make the decision” based on a predefined static set-point temperature, disregarding other important parameters, such as relative humidity, the position of windows and activity of occupants. Control algorithms that would account for a variety of parameters are of paramount importance to achieve energy savings whilst maintaining thermal comfort conditions. The aim of this research was to investigate the impact on thermal comfort and energy savings of novel and sophisticated control algorithms in mixed-mode residential buildings in India.Initially, it was important to identify all the control parameters that were important to be included in the control algorithms. Then the control algorithms were designed and presented in flow charts. To analyse the performance of the proposed control algorithms, computer simulations were performed, whilst a validation analysis was conducted to provide evidence of the validity of the control algorithms. Computer modelling comprised of co-simulations, using Dynamic Thermal Modelling (DTM) (EnergyPlus) and equation-based tools (Dymola using the Modelica language). The coupling of these was achieved using the Functional Mock-up Interface (FMI) for model exchange. The co-simulations enabled to examine the energy saving potential that can be achieved by the proposed control algorithms. In order to evaluate the ventilation performance of the proposed control algorithms, the ventilation rates and ventilation effectiveness of the systems were analysed using Computational Fluid Dynamics (CFD). This allowed the final analysis which included the evaluation of the ventilation performance of the control algorithms by calculating the ventilation effectiveness. To provide evidence of the proposed control algorithms and simulation approach, a validation study was done using data from an experimental chamber in India. This research has contributed to the existing body of knowledge by providing four main conclusions concerning the design and control of mixed-mode ventilation and cooling systems: i) to deliver comprehensive guidelines on the design and control of mixed-mode buildings, and the ways in which the co-simulations can be implemented to improve the existing control algorithms that can be found in the literature; ii) the use of the co-simulations showed that the developed control algorithms, when dampers/windows and ceiling fans are used, can improve the predicted hours of thermal comfort by up to 1900h compared to the scenarios when the ceiling fans were turned off, while achieving up to 55% energy reduction depending on the city; iii) the CFD simulations predicted that cross ventilation with the maximum opening areas for windows and dampers in combination with the operation of the ceiling fans can dillute the contaminants and/or heat in the building resulting in comfortable internal environments resulting in heat removal effectiveness of 1.65; and iv) the accurate and validated control algorithms that were developed in this research can be used for any study that requires control of mixed-mode buildings regardless of the geometry of the building. The use of co-simulations provides great flexibility since the same control algorithms can be used in any geometry or building location without the need for any modification of the code.</div

An Adaptive Intelligent Integrated Lighting Control Approach for High-Performance Office Buildings

abstract: An acute and crucial societal problem is the energy consumed in existing commercial buildings. There are 1.5 million commercial buildings in the U.S. with only about 3% being built each year. Hence, existing buildings need to be properly operated and maintained for several decades. Application of integrated centralized control systems in buildings could lead to more than 50% energy savings. This research work demonstrates an innovative adaptive integrated lighting control approach which could achieve significant energy savings and increase indoor comfort in high performance office buildings. In the first phase of the study, a predictive algorithm was developed and validated through experiments in an actual test room. The objective was to regulate daylight on a specified work plane by controlling the blind slat angles. Furthermore, a sensor-based integrated adaptive lighting controller was designed in Simulink which included an innovative sensor optimization approach based on genetic algorithm to minimize the number of sensors and efficiently place them in the office. The controller was designed based on simple integral controllers. The objective of developed control algorithm was to improve the illuminance situation in the office through controlling the daylight and electrical lighting. To evaluate the performance of the system, the controller was applied on experimental office model in Lee et al.’s research study in 1998. The result of the developed control approach indicate a significantly improvement in lighting situation and 1-23% and 50-78% monthly electrical energy savings in the office model, compared to two static strategies when the blinds were left open and closed during the whole year respectively.Dissertation/ThesisDoctoral Dissertation Architecture 201