Role of outdoor trees on pedestrian wind and thermal conditions around a pre-education building for sustainable energy management

Finding sustainable energy resources is essential to face the increasing energy demand.Trees are an important part of ancient architecture but are becoming rare in urban areas.Trees can control and tune the pedestrian-level wind velocity and thermal condition.In this study,a numerical investigation is employed to assess the role of trees planted in the windward direction of the building complex on the thermal and pedestrian wind velocity conditions around/inside a pre-education building located in the center of the complex.Compared to the previous studies(which considered only outside buildings),this work considers the effects of trees on microclimate change both inside/outside buildings.Effects of different parameters including the leaf area density and number of trees,number of rows,far-field velocity magnitude,and thermal condition around the main building are assessed.The results show that the flow velocity in the spacing between the first-row buildings is reduced by 30%-40% when the one-row trees with 2 m height are planted 15 m farther than the buildings.Furthermore,two rows of trees are more effective in higher velocities and reduce the maximum velocity by about 50%.The investigation shows that trees also could reduce the temperature by about 1℃around the building.

Low Carbon Building Design Optimization Based on Intelligent Energy Management System

The construction of relevant standards for building carbon emission assessment in China has just started,and the quantitative analysis method and evaluation system are still imperfect,which hinders the development of low-carbon building design.Therefore,the use of intelligent energy management system is very necessary.The purpose of this paper is to explore the design optimization of low-carbon buildings based on intelligent energy management systems.Based on the proposed quantitative method of building carbon emission,this paper establishes the quota theoretical system of building carbon emission analysis,and develops the quota based carbon emission calculation software.Smart energy management system is a low-carbon energy-saving system based on the reference of large-scale building energy-saving system and combined with energy consumption.It provides a fast and effective calculation tool for the quantitative evaluation of carbon emission of construction projects,so as to realize the carbon emission control and optimization in the early stage of architectural design and construction.On this basis,the evaluation,analysis and calculation method of building structure based on carbon reduction target is proposed,combined with the carbon emission quota management standard proposed in this paper.Taking small high-rise residential buildings as an example,this paper compares and analyzes different building structural systems from the perspectives of structural performance,economy and carbon emission level.It provides a reference for the design and evaluation of low-carbon building structures.The smart energy management system collects user energy use parameters.It uses time period and time sequence to obtain a large amount of data for analysis and integration,which provides users with intuitive energy consumption data.Compared with the traditional architectural design method,the industrialized construction method can save 589.22 megajoules(MJ)per square meter.Based on 29270 megajoules(MJ)per ton of standard coal,the construction area of the case is about 8000 m2,and the energy saving of residential buildings is 161.04 tons of standard coal.This research is of great significance in reducing the carbon emission intensity of buildings.

Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

Energy Management System with Power Offering Strategy for a Microgrid Integrated VPP

In the context of both the Virtual Power Plant (VPP) and microgrid(MG), the Energy Management System (EMS) is a key decision-maker forintegrating Distributed renewable Energy Resources (DERs) efficiently. TheEMS is regarded as a strong enabler of providing the optimized schedulingcontrol in operation and management of usage of disperse DERs and RenewableEnergy reSources (RES) such as a small-size wind-turbine (WT) andphotovoltaic (PV) energies. The main objective to be pursued by the EMSis the minimization of the overall operating cost of the MG integrated VPPnetwork. However, the minimization of the power peaks is a new objective andopen issue to a well-functional EMS, along with the maximization of profitin the energy market. Thus, both objectives have to be taken into accountat the same time. Thus, this paper proposes the EMS application incorporatingpower offering strategy applying a nature-inspired algorithm such asParticle Swarm Optimization (PSO) algorithm, in order to find the optimalsolution of the objective function in the context of the overall operating cost,the coordination of DERs, and the energy losses in a MG integrated VPPnetwork. For a fair DERs coordination with minimized power fluctuationsin the power flow, the power offering strategies with an active power controland re-distribution are proposed. Simulation results show that the proposedMG integrated VPP model with PSO-based EMS employing EgalitarianreDistribution (ED) power offering strategy is most feasible option for theoverall operating cost of VPP revenue. The total operating cost of the proposedEMS with ED strategy is 40.98$ compared to 432.8$ of MGs only withoutEMS. It is concluded that each MGs in the proposed VPP model intelligentlyparticipates in energy trading market compliant with the objective function,to minimize the overall cost and the power fluctuation.

Development of Energy Management System for Micro Grid Operation

The introduction of several small and large-scale industries,malls,shopping complexes,and domestic applications has significantly increased energy consumption.The aim of the work is to simulate a technically viable and economically optimum hybrid power system for residential buildings.The proposed micro-grid model includes four power generators:solar power,wind power,Electricity Board(EB)source,and a Diesel Generator(DG)set,with solar and wind power performing as major sources and the EB supply and DG set serving as backup sources.The core issue in direct current to alternate current conversion is harmonics distortion,a five-stage multilevel inverter is employed with the assistance of an intelligent control system is simulated and the optimum system configuration is estimated to reduce harmonics and improve the power quality.The monthly demand for residential buildings is 13-15 Megawatts.So,almost 433 Kilo-Watts(KW)of electricity is required every day,and if it is used for 8 h per day,50-60 KW of electricity is needed per hour.The overall micro-grid model’s operation and performance are established using MATLAB/SIMULINK software,and simulation results are provided.The simulation results show that the developed system is both cost-effective and environment friendly resulting in yearly cost reductions.

Determination of Effectiveness of Energy Management System in Buildings

Building Energy Management Systems(BEMS)are computer-based systems that aid in managing,controlling,and monitoring the building technical services and energy consumption by equipment used in the building.The effectiveness of BEMS is dependent upon numerous factors,among which the operational characteristics of the building and the BEMS control parameters also play an essential role.This research develops a user-driven simulation tool where users can input the building parameters and BEMS controls to determine the effectiveness of their BEMS.The simulation tool gives the user the flexibility to understand the potential energy savings by employing specific BEMS control and help in making intelligent decisions.The simulation is developed using Visual Basic Application(VBA)in Microsoft Excel,based on discrete-event Monte Carlo Simulation(MCS).The simulation works by initially calculating the energy required for space cooling and heating based on current building parameters input by the user in the model.Further,during the second simulation,the user selects all the BEMS controls and improved building envelope to determine the energy required for space cooling and heating during that case.The model compares the energy consumption from the first simulation and the second simulation.Then the simulation model will provide the rating of the effectiveness of BEMS on a continuous scale of 1 to 5(1 being poor effectiveness and 5 being excellent effectiveness of BEMS).This work is intended to facilitate building owner/energy managers to analyze the building energy performance concerning the efficacy of their energy management system.

Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest energy.Therefore,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become paramount.In this vein,efforts have been made to predict the HL and CL using a univariate approach.However,this approach necessitates two models for learning HL and CL,requiring more computational time.Moreover,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware requirement.In this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D CNN.Considering the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and CL.As the 1D data are not affected by excessive parameters,the pooling layer is not applied in this implementation.Besides,the use of pooling has been questioned by recent studies.The performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

FEDRP Based Model Implementation of Intelligent Energy Management Scheme for a Residential Community in Smart Grids Network

In the framework of liberalized deregulated electricity market, dynamic competitive environment exists between wholesale and retail dealers for energy supplying and management. Smart Grids topology in form of energy management has forced power supplying agencies to become globally competitive. Demand Response (DR) Programs in context with smart energy network have influenced prosumers and consumers towards it. In this paper Fair Emergency Demand Response Program (FEDRP) is integrated for managing the loads intelligently by using the platform of Smart Grids for Residential Setup. The paper also provides detailed modeling and analysis of respective demands of residential consumers in relation with economic load model for FEDRP. Due to increased customer’s partaking in this program the load on the utility is reduced and managed intelligently during emergency hours by providing fair and attractive incentives to residential clients, thus shifting peak load to off peak hours. The numerical and graphical results are matched for intelligent energy management scenario.

Improved Energy Management Strategy for Prosumer Buildings with Renewable Energy Sources and Battery Energy Storage Systems

The concept of utilizing microgrids(MGs)to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits.These pro-sumer buildings consist of renewable energy sources and usually install battery energy storage systems(BESSs)to deal with the uncertain nature of renewable energy sources.However,because of the high capital investment of BESS and the limitation of available energy,there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS.In this regard,this paper proposes an improved energy management strategy(IEMS)for the prosumer building to minimize the operating cost of MG and degradation factor of BESS.Moreover,to estimate the practical operating life span of BESS,this paper utilizes a non-linear battery degradation model.In addition,a flexible load shifting(FLS)scheme is also developed and integrated into the proposed strategy to further improve its performance.The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic(PV)and BESS-powered AC-DC hybrid MG installed at a commercial building.Moreover,the scenario reduction technique is used to handle the uncertainty associated with generation and load demand.To validate the performance of the proposed strategy,the results of IEMS are compared with the well-established energy management strategies.The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS.Moreover,FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS,thus making the operation of prosumer building more economical and efficient.

Generative pre-trained transformers(GPT)-based automated data mining for building energy management:Advantages,limitations and the future

Advanced data mining methods have shown a promising capacity in building energy management.However,in the past decade,such methods are rarely applied in practice,since they highly rely on users to customize solutions according to the characteristics of target building energy systems.Hence,the major barrier is that the practical applications of such methods remain laborious.It is necessary to enable computers to have the human-like ability to solve data mining tasks.Generative pre-trained transformers(GPT)might be capable of addressing this issue,as some GPT models such as GPT-3.5 and GPT-4 have shown powerful abilities on interaction with humans,code generation,and inference with common sense and domain knowledge.This study explores the potential of the most advanced GPT model(GPT-4)in three data mining scenarios of building energy management,i.e.,energy load prediction,fault diagnosis,and anomaly detection.A performance evaluation framework is proposed to verify the capabilities of GPT-4 on generating energy load prediction codes,diagnosing device faults,and detecting abnormal system operation patterns.It is demonstrated that GPT-4 can automatically solve most of the data mining tasks in this domain,which overcomes the barrier of practical applications of data mining methods in this domain.In the exploration of GPT-4,its advantages and limitations are also discussed comprehensively for revealing future research directions in this domain.

Economic Model Predictive Control for Hot Water Based Heating Systems in Smart Buildings

This paper presents a study to optimize the heating energy costs in a residential building with varying electricity price signals based on an Economic Model Predictive Controller (EMPC). The investigated heating system consists of an air source heat pump (ASHP) incorporated with a hot water tank as active Thermal Energy Storage (TES), where two optimization problems are integrated together to optimize both the ASHP electricity consumption and the building heating consumption utilizing a heat dynamic model of the building. The results show that the proposed EMPC can save the energy cost by load shifting compared with some reference cases.

A Novel Model of Intelligent Electrical Load Management by Goal Programming for Smart Houses, Respecting Consumer Preferences

Energy management is being highly regarded throughout the world. High-energy consumption in residential buildings is one of the dominant reasons of excessive energy consumption. There are many recent works on the demand-side management (DSM) and smart homes to keep control on electricity consumption. The paper is an intelligence to modify patterns, by proposing a time scheduling consumers, such that they can maintain their welfare while saving benefits from time varying tariffs;a model of household loads is proposed;constraints, including daily energy requirements and consumer preferences are considered in the framework, and the model is solved using mixed integer linear programming. The model is developed for three scenarios, and the results are compared: the 1st scenario aims Peak Shaving;the 2nd minimizes Electricity Cost, and the 3rd one, which distinguishes this study from the other related works, is a combination of the 1st and 2nd Scenarios. Goal programming is applied to solve the 3rd scenario. Finally, the best schedules for household loads are presented by analyzing power distribution curves and comparing results obtained by these scenarios. It is shown that for the case study of this paper with the implementation of 3rd scenario, it is possible to gain 7% saving in the electricity cost without any increasing in the lowest peak power consumption.

Discussion on the Intelligent Design of Ultra-Low Energy Consumption Passive Buildings

With the continuous development of science and technology and the gradual improvement of modem building technology, people pay more and more attention to the introduction of advanced technology in architectural design, such as the application of intelligent technology. With the increasingly severe environmental situation, people are increasingly demanding the environmental performance and green performance of buildings. The establishment of ultra-low energy consumption passive buildings has become one of the key construction contents of construction projects. This paper mainly analyzes the design points and architectural forms of related buildings from the perspective of intelligent control.

Modular energy cost optimization for buildings with integrated microgrid

Buildings are becoming suitable for application of sophisticated energy management approaches to increase their energy efficiency and possibly turn them into active energy market participants.The paper proposes a modular coordination mechanism between building zones comfort control and building microgrid energy flows control based on model predictive control. The approach opens possibilities to modularly coordinate technologically heterogeneous building subsystems for economically-optimal operation under user comfort constraints. The imposed modularity is based on a simple interface for exchanging building consumption and microgrid energyprice profiles. This is a key element for technology separation,replication and up-scaling towards the levels of smart grids and smart cities where buildings play active roles in energy management. The proposed coordination mechanism is presented in a comprehensive realistic case study of maintaining comfort in an office building with integrated microgrid. The approach stands out with significant performance improvements compared to various non-coordinated predictive control schemes and baseline controllers. Results give detailed information about yearly cost-effectiveness of the considered configurations,which are suitable for deployment as short-and long-term zero-energy building investments.

Artificial Intelligence Technique and Wireless Sensor Networks in En­ergy Management System for Secure Power Optimization

The modern basic building blocks of a control system consist of data acquisition,dispensation of data by the system operators and the remote control of system devices.However,the physical controls,technical ex­aminations and deductions were originally implemented to aid the process and control of power system design.The complexity of the power system keeps increasing due the technical improvements,diversity and dynamic requirements.Artificial intelligence is the science of automating intelligent activities presently attainable by individuals.Intelligent system techniques may be of excessive benefit in the application of area power system con­trols.Whereas smart grid can be measured as a modern electric power grid structure for better productivity and dependability via automatic control,excessive power converters,modern communications setup,sensing and metering equipment,and modern energy management techniques estab­lished on the optimization of demand,energy and network accessibility,and so on.The enormous depiction of the entire transmission grid,in the perspective of smart grids,is quite unclear;and in Nigeria no studies have been put on ground in order for the existing network to be turn into a smart grid.In this research work emphasis is placed on generation and transmis­sion stations;power optimization using artificial intelligent techniques and wireless sensor networks for power control management system.

Analysis on Reformation of Digital Management Education of Prefabricated Construction Project Under the Background of Intelligent Construction

Prefabricated building is an important building development mode at present.It can improve the development speed and production efficiency of the construction industry,reduce the labor intensity of construction,play an important role in the development as well as the modernization of the construction industry.Due to the late development of prefabricated buildings,the talent training of prefabricated buildings lags behind,causing the development of prefabricated buildings to not achieve its required target.Therefore,the characteristics of intelligent construction need to be incorporated,the education of prefabricated buildings needs to be reformed,the education regarding digital management of prefabricated buildings should be improved,and more professional talents should be nurtured.

Stochastic and Distributed Optimal Energy Management of Active Distribution Networks Within Integrated Office Buildings

This paper proposes a stochastic and distributed optimal energy management approach for active distribution networks(ADNs)within office buildings.The proposed approach aims at scheduling office buildings fitted with heating ventilation and air conditioning(HVAC)systems,and electric vehicle(EV)charging piles,to participate in the ADN optimization.First,an energy management approach for the ADN with aggregated office buildings is proposed.And the ADN optimization model is formulated considering the detailed building thermal dynamics and the mobile behaviors of workers.Then,to consider un-certainties of photovoltaic(PV)power,scenario-based stochastic programming is integrated into the ADN optimization model.To further realize the stochastic energy management of the ADN within office buildings in a distributed manner,the alternating direction method of multipliers(ADMM)is used to solve the ADN optimization model.The original ADN optimization problem is divided into the network-side and the building-side sub-problems to effectively protect the privacy of the ADN and the office buildings.Finally,the ADN optimization model incorporating office buildings is validated in the winter by numerical studies.In addition,the impacts of comfort temperature range and expected state of charge(SOC)at departure are analyzed.Index Terms—ADN,EV,HVAC system,Office building,Stochastic and distributed energy management.

Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces

Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.

Conceptual Issues in the Qualification of Intelligent Buildings

The reality of global warming must have been settled by now while the incidence of same has in very recent times adopted unprecedented dimensions. The global community continues to look for ways to combat the impact of climate change and technology is looked upon to deliver the innovations that would ensure a better tomorrow today. Rapid advancement of Information Technology (IT), is now transforming the way we create and interact with the built environment with the notion of Intelligent Buildings (IBs) underscoring its main features. However, these IBs utilize systems that require energy, and fossil fuels are currently the world’s primary energy sources;they can also irreparably harm the environment, exacerbating climate change. What then is the true essence of IBs? This paper, through review of existing literature, attempts to explore some issues associated with the conceptualization of IBs, highlighting how they are similar with other notional options that deliver the same benefits but without the needed IT systems or the energy required in running them. It also discusses the need to focus on less energy demanding and management approaches at design or occupancy of buildings as a way to reduce the demand and thus consumption of fossil fuels across the world.

Development of intelligent buildings in Bahrain

There is a growing demand for high building performance,providing better energy efficiency as well as high standard living and workplace. Intelligent Buildings (IBs) will meet the needs of sustainable development in built environment. The Kingdom of Bahrain's construction industry has grown significantly in past years to support economic growth,other business sectors,and the aim to be one of the world's major financial centres. This paper aims at investigating current issues regarding intelligent buildings in the Kingdom of Bahrain. Case studies of three buildings,which are Bahrain Financial Harbour (BFH),Bahrain World Trade Centre (BWTC) Project and the Bahrain City Centre (BCC) Shopping Mall,have been carried out via structured observation,interview and questionnaire survey in Bahrain in 2007. Through this study,we conclude that the pace of construction in Bahrain and the Middle East is such that there are lots of scopes for IBs to develop further,both from the perspective of the number of IBs being built and also the sophistication of their specifications.