Residential Building Energy Management Research Articles

Smart energy management in residential buildings: the impact of knowledge and behavior

A new technology called smart energy management makes use of IoT concepts to enhance energy efficiency and lower waste in structures. The goal of this study is to comprehend how household energy management knowledge affects energy usage, user behavior, related expenses, and environmental effect. Through a survey of 100 valid replies in Palestine, the research model assessed the knowledge and consumption habits of building occupants. Smart PLS software was used to analyze the research model using partial least squares structural equation modeling (PLS-SEM). Using path coefficients and behavior as a mediating variable, the structural model connected the latent variables. The mediation hypotheses were tested using the Preacher and Hayes method, and the indirect effect and confidence intervals were estimated and calculated using bootstrapping. The findings demonstrated that by lowering energy use and enhancing overall building performance, residential buildings that implement smart energy consumption management systems may move toward a more sustainable future. Furthermore, the study found that education and awareness campaigns are necessary to increase residents’ knowledge of these systems to promote energy savings. The results also indicated statistically significant indirect effects, supporting the existence of mediation of the behavior construct. Path coefficient values and P-values were presented to further support the study’s hypotheses. Such smart energy management systems represent an important innovation in building management and can help create more sustainable and efficient buildings.

Influence of the Longitudinal and Tree-Shaped Fin Parameters on the Shell-and-Tube LHTES Energy Efficiency

Changes in the energy sector, associated with the move away from fossil fuels, pose a challenge for appropriate thermal energy management in residential buildings. The important element to deal with the variability of renewable energy in thermal systems is latent heat thermal energy storage. Due to the low thermal conductivity of phase change materials, a number of techniques are proposed to enhance the heat transfer process. In this research, the global sensitivity of fin geometrical parameters on the melting and solidification times and energy efficiency of these processes was investigated. The computational model of the phase change was developed using the finite volume method with the enthalpy-porosity model and Boussinesq approximation. Numerical simulations were carried out according to the design of experiments technique. The multi-dimensional response surface was developed, and the multi-objective optimisation was done. The research shows that the melting process is most influenced by the position of the top fin (α angle) and the solidification process by the position of the bottom fin (γ angle). The angle of the tree fin (β) has a different effect on both processes, with the energy efficiency decreasing during melting and increasing during solidification. Maximum values for the energy efficiencies of melting (ηm=0.973) and solidification (ηs=0.988) were obtained for α=18.2°, β=89.0°, L=10.7 mm and γ=21.0°.

Two-stage residential community energy management utilizing EVs and household load flexibility under grid outage event

To relieve the impact of the power grid outages on the residential sector, residential building energy management under grid outage events has been becoming the research hotspot in both the academic and industry. Considering the coordination of electric vehicles (EVs) and household load flexibility, this paper proposes a two-stage energy management approach for residential community under the planned outages. In the optimal scheduling stage, after receiving the information of the planned outage from the grid, the EVs’ charging–discharging power and community load curve reshaping schemes are optimal determined by residential community energy management system (CEMS), aiming at to minimize the total amount of unserved load of the whole community over the planned outage horizon. In the power allocation stage, with the formulated power allocation model, the determined residential community load curve from the above stage is then allocated to each house. The numerical test is finally conducted and the results validated the effectiveness and feasibility of the proposed approach.

Impact of battery degradation on energy cost and carbon footprint of smart homes

Optimal energy management in residential buildings with battery storage devices largely relies on the implementation of accurate battery degradation models. A proper wear model to use for the battery scheduling at homes should be universally applicable to different battery technologies and precisely model the impact of the major parameters that contribute to the capacity loss. In this work, a technology-agnostic battery wear model has been proposed to address the degradation caused by the irregular cycling of batteries in residential buildings. The proposed wear model provides the DoD-associated degradation for charging/discharging events between random SoCs only by employing the lifecycle curve of batteries which is usually provided by battery manufactures. This model has been formulated in the framework of mixed-integer programming (MIP) such that it can be directly incorporated into MIP optimization models to provide optimal unit commitment solutions without sacrificing other objectives of the problem. To facilitate the implementation of the nonlinear wear coefficient in MIP models, a curve-fitted version of the wear coefficient is used in the MIP problem. A comprehensive study on the costs and carbon footprint of a smart home has been carried out in this paper by producing a MIP problem that incorporates the proposed battery wear model. The problem has been solved with different approaches to minimize costs, capacity loss, and emissions of the system. The optimization results show that the application of the presented wear model managed to lower the cost, carbon footprint, and battery degradation by 78%, 30%, and 81% respectively.

Uncertainty-Aware Model Predictive Control for Residential Buildings Participating in Intraday Markets

Buildings with generation and storage assets have the opportunity to trade in electricity markets. Residential buildings, however, have load patterns that are more difficult to predict and less flexible – introducing uncertainties to their operation. In addition, local intermittent renewable energy sources further increase these uncertainties. In this regard, an uncertainty-aware model predictive control (UA-MPC) is proposed in this paper. The proposed UA-MPC allows residential building energy management systems to trade in intraday markets without violating the operational constraints of their batteries despite the uncertainties in load consumption and solar irradiation. Moreover, the proposed UA-MPC only needs prediction intervals instead of detailed probability distribution functions to describe the uncertainties. Furthermore, in the proposed UA-MPC, the optimization is formulated as a shortest path problem. Thus, the optimization can be solved in polynomial time, which is desirable in intraday settings. Numerical simulations for two active residential buildings in grid-connected and isolated-neighborhood scenarios have been used to evaluate the proposed UA-MPC. Furthermore, the performance of the proposed UA-MPC was compared with the performance of a deterministic model predictive control (MPC) and a robust MPC. The results show that the proposed UA-MPC eliminates constraint violations that would otherwise occur in using deterministic MPC while providing lower costs than those using robust MPC. The results also show that the proposed UA-MPC can generate bidding curves in a few seconds, demonstrating its applicability in intraday market settings.

Internet of things‐based smart residential building energy management system for a grid‐connected solar photovoltaic‐powered DC residential building

The intermittent nature of photovoltaic (PV), energy conversion losses, appliance compatibility, and load dynamics pose serious challenges to PV-based DC power distribution system in residential buildings. This article proposes a holistic approach to develop a new smart residential building energy management system using internet of things (IoT)-based multifunction compatible relaying system. The proposed system monitors the loads and provides uninterruptible power supply with minimum energy conversion losses. It achieves best operating load configuration, load prioritization in various modes, and demand-side management (DSM). IoT-based multifunction compatible relaying dystem is embedded with Information and Communication Technology-enabled PZEM module, smart relays, and appliance status monitoring devices. The proposed scheme has been implemented in a DC residential building located at Madurai, Tamil Nadu, India, with 1.5 kW PV system. The results are compared in four scenarios. The experimental results prove that the losses are minimized by 18% ensuring uninterrupted power supply. The implementation of DSM further reduces the losses by 5%. The appliances can be monitored through Grafana based in home touch screen.

Carbon Footprint of Residents’ Housing Consumption and Its Driving Forces in China

A large population size and rapid economic growth have resulted in a huge amount of housing consumption in China. Therefore, it is critical to identify the determinants of housing carbon footprint (CF) and prepare appropriate carbon mitigation measures. By employing the IPCC accounting method, input-output analysis and the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, this study aims to study the spatio-temporal patterns and identify the driving factors of housing CF. The results show that regional disparities and urban-rural differences existed during the period 2012–2017. The results of the extended STIRPAT model show that population scale and energy consumption per unit building area are the two dominant contributors to the housing CF increments in all areas. While, family size only shows significant negative impact in eastern and western regions, the per capita disposable income only induces higher housing CF in rural areas, and energy structure had a remarkable positive impact in urban area of western region and all rural areas. Policy recommendations are proposed to mitigate the overall housing CF, including; controlling population growth and promoting urbanization benefits; encouraging green consumption; optimizing household energy consumption structure, and; enhancing residential building energy management.

Fuzzy Control System for Smart Energy Management in Residential Buildings Based on Environmental Data

Modern energy automation solutions and demand response applications rely on load profiles to monitor and manage electricity consumption effectively. The introduction of smart control systems capable of handling additional fuzzy parameters, such as weather data, through machine learning methods, offers valuable insights in an attempt to adjust consumer behavior optimally. Following recent advances in the field of fuzzy control, this study presents the design and implementation of a fuzzy control system that processes environmental data in order to recommend minimum energy consumption values for a residential building. This system follows the forward chaining Mamdani approach and uses decision tree linearization for rule generation. Additionally, a hybrid feature selector is implemented based on XGBoost and decision tree metrics for feature importance. The proposed structure discovers and generates a small set of fuzzy rules that highlights the energy consumption behavior of the building based on time-series data of past operation. The response of the fuzzy system based on sample input data is presented, and the evaluation of its performance shows that the rule base generation is derived with improved accuracy. In addition, an overall smaller set of rules is generated, and the computation is faster compared to the baseline decision tree configuration.

Data Driven Optimization of Energy Management in Residential Buildings with Energy Harvesting and Storage

This paper presents a battery-aware stochastic control framework for residential energy management systems (EMS) equipped with energy harvesting, that is, photovoltaic panels, and storage capabilities. The model and control rationale takes into account the dynamics of load, the weather, the weather forecast, the utility, and consumer preferences into a unified Markov decision process. The embedded optimization problem is formulated to determine the proportion of energy drawn from the battery and the grid to minimize a cost function capturing a user-defined tradeoff between battery degradation and financial expense by user preferences. Numerical results are based on real-world weather data for Golden, Colorado, and load traces. The results illustrate the ability of the system to limit battery degradation assessed using the Rain flow counting method for lithium ion batteries.

Multiagent Reinforcement Learning for Energy Management in Residential Buildings

The aim of this article is to explore the multiagent reinforcement learning approach for residential multicarrier energy management. Defining the multiagents system not only enhances the possibility of dedicating separate demand response programs for different components but also accelerates the computational calculations. We employ the Q -learning to provide the optimum solution in solving the presented residential energy management problem. Furthermore, to address uncertainties, a scenario-based method with the real data and proper probability density functions is used. Deterministic and stochastic numerical calculations are made to justify the effectiveness and robustness of the proposed method. The simulated results indicate that the application of the proposed reinforcement learning-based method leads to lower cost schemes for consumers rather than the conventional optimization-based energy management programs.

Energy management in residential buildings using energy hub approach

Building-owned micro energy hubs (EHs) usually focus on optimal energy consumption cost and emission, whereas, macro energy hubs (MEHs) mainly concentrate on utility’s interest termed as network load deviation. Therefore, a bi-level MEH control capable of simultaneously attaining bilateral stakes is required. However, in real life energy distribution systems, MEH structure operates under uncertainties such as unpredictable solar photovoltaic (PV) irradiance and unplanned electric and natural gas (NG) network outages. Such uncertain conditions may affect the MEH’s performance (energy cost, emission and network load deviation) and resilience (capability of recovering quickly from disturbances) undesirably. Objective of this paper is to attain an optimal compromise between the performance and the resilience of a bi-level residential MEH, under uncertain conditions. In first step, risk neutral bi-level MEH is proposed to optimally reduce the network load deviation, while obeying the customer specified comfort, emission and cost constraints. However, this strategy disregards the risk introduced by the uncertainties. In second step, risk averse strategy is devised by incorporating conditional value at risk (CVaR) in the objective function, for improvement in resilience. Proposed linear bi-level risk averse MEH is mapped in conventional flower pollination algorithm (FPA). Resilience of the MEH is measured in terms of energy stored in the plug-in hybrid electric vehicle (PHEV) and thermal energy storage (TES). Third step concentrates on the development of an efficient solution technique for energy management system to obtain better solution. For this, an improved version of FPA, termed as 2-cored FPA is proposed and employed to solve the model. Proposed technique has a filtration layer (termed as core-1) that consists of random walk, local pollination and global pollination to obtain improved pollinators. Subsequently, these pollinators are injected into conventional FPA layer (termed as core-2) to obtain better solution. Comparison of results demonstrates that under risk averse approach with two cores, the energy retaining capability of the PHEV and the TES increases by 31.66% and 57.66%, respectively.

Improved energy management technique in pipe-embedded wall heating/cooling system in residential buildings

Effective and environmentally responsive techniques of energy management in residential buildings are desirable for the resulting reduction of energy costs and consumption. In this paper, an improved and efficient technique of energy management in pipe-embedded wall heating/cooling systems, called the Thermal Barrier, is described. Specifically, the Thermal Barrier is a technique focused on the management and control of heat supply into and heat extraction from external walls containing embedded pipes. The installed pipe-embedded wall heating/cooling system is fully controlled by a special fuzzy logic program that synchronizes the heat supply/extraction with variable heat loads. The main operation rule of the Thermal Barrier is to keep changes of the wall internal energy close to zero for the given reference temperature of a pseudo-surface created by an embedded pipe system of the wall heat exchanger. Comprehensive field measurement results associated with an example Thermal Barrier System installed in a residential two-story house are presented. These measurements confirmed the high-efficiency of the Thermal Barrier and its ability to use low-grade heat sources and sinks to effectively control an indoor climate. The supply water temperature was very low (25.3 °C) in the winter and very high (20.5 °C) in the summer. Daily variations of the indoor air temperature did not exceed 0.8 °C throughout the year. During the summer, the Thermal Barrier System operated in cooling-mode only from a low-grade renewable heat sink. The flexibility of the Thermal Barrier also allows for using heat sources/sinks different from those in the test house.

Model predictive control for building loads connected with a residential distribution grid

Aggregated control of electrical loads in a large cluster of buildings has been a challenge due to the complexity of the system involving generators, grid constraints, load serving entities complex load models, and people behavior. This paper introduces a novel load aggregation method in an electricity distribution system with Model Predictive Controlled (MPC) loads. This method closes the control loop from power generation to people behavior, resulting in a more stable and efficient integrated buildings-to-grid system. A behavior-driven price-based MPC is introduced for a residential building energy management system, which controls the air conditioner (AC), electric vehicle (EV), water heater, and battery energy storage system. A nodal pricing method is introduced representing power generation and distribution costs, which is mathematically proven to stabilize the system with MPC controlled loads. The method is tested in a 342-node residential building distribution network with 15,000 buildings which is inverse sampled from hundreds of actual smart meter data. The results show a 21% reduction in generation cost, 17% reduction in peak load, and reduced nodal voltage drop from the coordinated control system.

Low complexity approach for energy management in residential buildings

Low complexity approach for energy management in residential buildings

Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic

Energy management in residential buildings has grabbed the attention of many scientists for the last few years due to the fact that the residential sector consumes the highest amount of total energy produced by different energy resources. To manage the energy in residential buildings effectively, an efficient energy control system is required, capable of decreasing the total energy consumption without compromising the user-preferred environment inside the building. In the literature, many approaches have been proposed to achieve the goals of minimizing the energy consumption and maximizing the user preferred comfort by keeping different parameters under consideration, but all these methods face some problems in resolving the issue properly. The bat algorithm is one of the most recently introduced optimization approaches that has drawn the attention of researchers to apply it for solving different types of optimization problems. In this paper, the bat algorithm is applied for energy optimization in residential buildings, which is one of the most focused optimization problems in recent years. Three environmental parameters, namely temperature, illumination and air quality are bat algorithm inputs and optimized values of these parameters are the outputs. The error difference between the environmental parameters and optimized parameters are inputs of the fuzzy controllers which give energy as output which in turn change the status of the concerned actuators. It is proven from the experimental results that the proposed approach has been effectively successful in managing the whole energy consumption management system.

Occupant-behavior driven appliance scheduling for residential buildings

Building-to-grid integration is one important step in having a smart grid. This integration will support energy efficiency, load balancing, and the incorporation of renewables. This study introduces a residential building energy management solution to control connected devices based on economic signals from the grid and occupant behavior. An air-conditioning unit, a water heater, and an electric vehicle (EV) were modeled and controlled using a traditional on/off controller and model predictive controller (MPC). The MPC is designed to minimize the total building electricity costs while maintaining occupant comfort. The MPC utilizes the building thermal mass, the EV battery storage, and the water heater’s hot water tank to shift electricity use to a period when the price is lower. This controller considers occupant behavior as a constraint in controlling these devices to maintain users’ comfort. The simulation results show a 10%–30% reduction in the electricity bill by applying the MPC in a dynamic electricity price scenario, as compared to the traditional methods of control. The proposed method of control makes buildings responsive to grid signals that provide the potential of peak shaving and ancillary services.

An Efficient Approach for Energy Consumption Optimization and Management in Residential Building Using Artificial Bee Colony and Fuzzy Logic

The energy management in residential buildings according to occupant’s requirement and comfort is of vital importance. There are many proposals in the literature addressing the issue of user’s comfort and energy consumption (management) with keeping different parameters in consideration. In this paper, we have utilized artificial bee colony (ABC) optimization algorithm for maximizing user comfort and minimizing energy consumption simultaneously. We propose a complete user friendly and energy efficient model with different components. The user set parameters and the environmental parameters are inputs of the ABC, and the optimized parameters are the output of the ABC. The error differences between the environmental parameters and the ABC optimized parameters are inputs of fuzzy controllers, which give the required energy as the outputs. The purpose of the optimization algorithm is to maximize the comfort index and minimize the error difference between the user set parameters and the environmental parameters, which ultimately decreases the power consumption. The experimental results show that the proposed model is efficient in achieving high comfort index along with minimized energy consumption.

Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System

The concern of energy price hikes and the impact of climate change because of energy generation and usage forms the basis for residential building energy conservation. Existing energy meters do not provide much information about the energy usage of the individual appliance apart from its power rating. The detection of the appliance energy usage will not only help in energy conservation, but also facilitate the demand response (DR) market participation as well as being one way of building energy conservation. However, energy usage by individual appliance is quite difficult to estimate. This paper proposes a novel approach: an unsupervised disaggregation method, which is a variant of the hidden Markov model (HMM), to detect an appliance and its operation state based on practicable measurable parameters from the household energy meter. Performing experiments in a practical environment validates our proposed method. Our results show that our model can provide appliance detection and power usage information in a non-intrusive manner, which is ideal for enabling power conservation efforts and participation in the demand response market.

MPC-Based Appliance Scheduling for Residential Building Energy Management Controller

This paper proposes an appliance scheduling scheme for residential building energy management controllers, by taking advantage of the time-varying retail pricing enabled by the two-way communication infrastructure of the smart grid. Finite-horizon scheduling optimization problems are formulated to exploit operational flexibilities of thermal and non-thermal appliances using a model predictive control (MPC) method which incorporates both forecasts and newly updated information. For thermal appliance scheduling, the thermal mass of the building, which serves as thermal storage, is integrated into the optimization problem by modeling the thermodynamics of rooms in a building as constraints. Within the comfort range modeled by the predicted mean vote (PMV) index, thermal appliances are scheduled smartly together with thermal mass storage to hedge against high prices and make use of low-price time periods. For non-thermal appliance scheduling, in which delay and/or power consumption flexibilities are available, operation dependence of inter-appliance and intra-appliance is modeled to further exploit the price variation. Simulation results show that customers have notable energy cost savings on their electricity bills with time-varying pricing. The impact of customers' preferences of appliances usage on energy cost savings is also evaluated.