Design Of Building Energy Systems Research Articles

Landlord–Tenant Dilemma: How Does the Conflict Affect the Design of Building Energy Systems?

To achieve climate goals, the European Union needs to increase building renovation rates. In owner-occupied buildings, energy cost savings provide financial incentives for renovation. However, 30% of all Europeans live in rented property, where conflicting stakeholder interests arise. Landlords are responsible for renovation decisions (building envelope and energy system) and the corresponding investments. Tenants face rising rents and only slightly benefit from falling energy costs. The literature calls this conflict the landlord–tenant dilemma. However, publications lack a quantification, leaving gaps in understanding its impact on technology choices and the heat transition. To address this, we incorporate the perspectives of landlords and tenants in a model-based approach for optimized technology choice (mixed-integer linear program). We compare optimal individual technology choices with the total cost optimum (including costs for landlords and tenants) for renovation decisions. Additionally, we examine how changes in the regulatory framework affect the economically driven landlord’s technology choice. Our study reveals that total costs and emissions are up to 60% and 283% higher for landlords deciding for rented houses compared to owner-occupied properties. Current approaches to solve the dilemma partly favor the development of climate-friendly energy systems. However, the renovation of the building envelope and operation costs are mostly disregarded in the decisions of landlords.

Study of the Relationship Between Temperature Change and Energy Transfer in Thermodynamic Processes in Buildings

In this paper, the relationship between room temperature and outside temperature, wall temperature, system operation, and energy consumption is investigated through mathematical modeling and simulation experiments. The thermodynamic parameters of a typical room are used in the study, and the characteristics of the heating and cooling system are considered. 1. This paper analyses the variation of room temperature, wall temperature, switching state, and heating power with time. The results show that the room and wall temperatures are influenced by the external temperature and system operation, while the switching state and heating power are regulated by the room temperature. The correlation between temperature and heating power is quantified by calculating the correlation coefficient matrix. The results show that there is a positive correlation between room temperature and heating power, while there is a negative correlation between wall temperature and room temperature and heating power. 2. This paper investigates the effect of external temperature on room temperature and wall temperature. The results show that an increase in external temperature leads to a decrease in room temperature and wall temperature. In addition, it is found that the indoor temperature is more sensitive to changes in external temperature within the range of changes in external temperature. 3. It is based on the steady-state solution curves between temperature and external temperature, as well as thermodynamic plots of system operation and energy consumption. The visual presentation of the effect of external temperature on the system provides a reference for optimizing building energy use and designing efficient heating and cooling systems. This study provides insight into the relationship between temperature change and energy transfer in building thermodynamic processes, guiding for achieving sustainable energy utilization and reducing environmental impacts. It is of great significance for optimizing the design of building energy systems and improving the efficiency of energy use.

Adaptive Optimization Design of Building Energy System for Smart Elderly Care Community Based on Deep Deterministic Policy Gradient

In smart elderly care communities, optimizing the design of building energy systems is crucial for improving the quality of life and health of the elderly. This study pioneers an innovative adaptive optimization design methodology for building energy systems by harnessing the cutting-edge capabilities of deep reinforcement learning. This avant-garde method initially involves modeling a myriad of energy equipment embedded within the energy ecosystem of smart elderly care community buildings, thereby extracting their energy computation formulae. In a groundbreaking progression, this study ingeniously employs the actor–critic (AC) algorithm to refine the deep deterministic policy gradient (DDPG) algorithm. The enhanced DDPG algorithm is then adeptly wielded to perform adaptive optimization of the operational states within the energy system of a smart retirement community building, signifying a trailblazing approach in this realm. Simulation experiments indicate that the proposed method has better stability and convergence compared to traditional deep Q-learning algorithms. When the environmental interaction coefficient and learning ratio is 4, the improved DDPG algorithm under the AC framework can converge after 60 iterations. The stable reward value in the convergence state is −996. When the scheduling cycle of the energy system is between 0:00 and 8:00, the photovoltaic output of the system optimized by the DDPG algorithm is 0. The wind power output fluctuates within 50 kW. This study realizes efficient operation, energy saving, and emission reduction in building energy systems in smart elderly care communities and provides new ideas and methods for research in this field. It also provides an important reference for the design and operation of building energy systems in smart elderly care communities.

Building Energy Saving for Indoor Cooling and Heating: Mechanism and Comparison on Temperature Difference

Reducing the heat transfer temperature difference via reasonable indoor temperature determination and air conditioning system design is a confirmed building energy-saving approach for space cooling and heating. However, the energy-saving mechanism cannot be explained scientifically and comprehensively while maintaining the cognitive level of the heat transfer law. In this paper, based on the same climatic conditions and decreasing range of indoor and outdoor temperature difference, the yearly and monthly absolute energy-saving amount (ESA) and relative energy-saving ratio (ESR) are investigated and compared for cooling and heating, respectively, to reveal the energy-saving mechanism for cooling and heating from the microscopic perspective. Two new concepts, including ESA by temperature difference and behavioral ESA by measure itself, are defined. The yearly ESA for cooling or heating caused by the decreasing of temperature difference is composed of those two factors. For cooling, the contribution rate of the behavioral ESA at those moments within the decreasing range of the temperature difference can be up to 78%, while for heating is only 7%. This work can provide theoretical support for building energy system design optimization and method reference for energy-saving analysis of building air conditioning systems with temperature difference considerations for cooling and heating, respectively.

Peak load reduction and resilience benefits through optimized dispatch, heating and cooling strategies in buildings with critical microgrids

This research analyzes the combined peak reduction and resilience increase in critical buildings through the use of microgrids configured using a specific control system to provide correct power quality for local loads that can be either connected or disconnected from the grid, operating grid-connected or islanded mode. In case of grid failure, energy storage combined with one or several local generators can provide backup power and consider both conventional and renewable energy systems. This research focuses on the design of building energy systems that are able to maintain the energy supply and establishes a methodology to evaluate the resilience benefits of a microgrid integrated into critical buildings when power outages occur and the particular case of a hospital building is presented. The optimization of the dispatch and heating and cooling strategies are analyzed and a case study characterizing an electric polygeneration microgrid feeding critical loads is presented. Results show the benefits and the increased energy resilience achieved when using solar PV, electrochemical batteries, combined heat and power, TES water tanks, and absorption chillers and propose a design and optimization scheme that can be applied for similar buildings and extend to any facility with critical loads. Results show that these microgrids can be optimally designed to improve the resilience of critical energy systems and, simultaneously, achieve economic benefits. Results show that in the event of an outage the positive monetary effects last longer than the duration of the outage.

Machine Learning Approach to Predict Building Thermal Load Considering Feature Variable Dimensions: An Office Building Case Study

An accurate and fast building load prediction model is critically important for guiding building energy system design, optimizing operational parameters, and balancing a power grid between energy supply and demand. A physics-based simulation tool is traditionally used to provide the building load demand; however, it is constrained by its complex model development process and requirement for engineering judgments. Machine learning algorithms (i.e., data-driven models) based on big data can bridge this gap. In this study, we used the massive energy data generated by a physics-based tool (EnergyPlus) to develop three data-driven models (i.e., LightGBM, random forest (RF), and long-short term memory (LSTM)) and compared their prediction performances. The physics-based models were developed using office prototype building models as baselines, and ranges were provided for selected key input parameters. Three different input feature dimensions (i.e., six-, nine-, and fifteen-input feature selections) were investigated, aiming to meet different demands for practical applications. We found that LightGBM significantly outperforms the RF and LSTM algorithms, not only with respect to prediction accuracy but also in regard to computation cost. The best prediction results show that the coefficient of variation of the root mean squared error (CVRMSE), squared correction coefficient (R2), and computation time are 5.25%, 0.9959, and 7.0 s for LightGBM, respectively, evidently better than the values for the algorithms based on RF (18.54%, 0.9482, and 44.6 s) and LSTM (22.06%, 0.9267, and 758.8 s). The findings demonstrate that a data-driven model is able to avoid the process of establishing a complicated physics-based model for predicting a building’s thermal load, with similar accuracy to that of a physics-based simulation tool.

Multi-objective building energy system optimization considering EV infrastructure

With increasing concerns over carbon dioxide emissions, the concept of Zero Energy Building (ZEB) has emerged. Electric Vehicles (EVs) are also considered environmentally friendly since they reduce greenhouse gas emissions, with a rapidly growing market. With these global trends of increasing EV amount and infrastructure, building energy systems should incorporate the ZEB concept and the increasing electricity requirements for EV charging. However, it is unclear how EV charging demand can affect building energy system design while aligning with ZEB requirements. Therefore, this paper develops a new framework to find the optimal energy system design that meets EV charging demand and ZEB requirements. The charging demand for EVs is predicted by the machine learning model, which combines the building energy demand from EnergyPlus. Ultimately, the Genetic Algorithm and PROBID method are applied to optimize the Total Annual Cost (TAC) and Self-Energy Sufficiency Ratio. EV charging demand has been found to affect energy system design, especially in small-size buildings. Using the proposed method, the building owner can determine the optimal capacity of an energy system based on economic and ZEB conditions, contributing to the future net ZEB and transportation systems.

Empirical modelling of variable capacity air-to-air heat pumps in trnsys

Variable capacity heat pumps improve seasonal performance and operate at lower temperatures by varying their compressor speed to match their capacity to a specific thermal load. They constitute a great asset in our struggle to lower energy consumption and carbon emissions, especially in cold climates. However, being a more recent and complex technology, they are not easily integrated in the design of building energy systems as adequate models are lacking. A trnsys model for variable capacity air-to-air heat pumps (vcaahps) relying on performance maps and restricted to mini-split systems was therefore developed to streamline this design process and provide more accurate results. The model is presented first; it can reproduce most common heat pump behaviours – such as defrost and latent cooling. Then, a methodology relying on experimental data to supplement (often incomplete) manufacturer performance maps is provided. Finally, model usage is exemplified through a case study highlighting specific features and behaviours.

Influences of usage intensity and weather on optimal building energy system design with multiple storage options

In non-residential buildings, many factors affect thermal and electrical loads. Some of these factors are not known during the planning process but are essential for a proper energy system design.Conducting a simulation- and optimization-based sensitivity analysis, this study investigates the effects of usage intensity and weather uncertainties on building energy system sizing. We developed a toolchain that comprehensively covers the stages, from determining user-related electricity demand to thermal demand calculation and energy conversion system design, up to deterministic design optimization of storage systems. By means of design of experiments, we systematically applied the toolchain to an all-electric non-residential building as a use case.We find that weather has the strongest correlation with optimal storage sizes and the size of energy conversion systems. They are also affected by the usage intensity of certain building zones. In addition, the storage sizes influence each other in the complex building energy system of our use case. Depending on the scenario and objective, annuities deviate 6%, annual emissions deviate up to 10% from the base scenario.We conclude that normative design methods do not suffice for optimal building energy system design and offer a viable method that can be incorporated into existing planning processes.

Framework for effective robust design of building energy systems: Bridging the gap between predicted and actual energy use

Practitioners working on building performance simulations do seldom apply uncertainty and sensitivity analysis using state-of-the-art global methods in their daily workflow. With stricter building regulations imposed year by year, a corresponding change in workflow has been identified as one important way to bridge the increasing gap between predicted and actual energy use. This study describes how statistical methods from research can be adapted to an efficient workflow in a practical setting, concretised through description of a proposed framework and methodology, and application on a case study for a hospital in Northern Norway.

Comparison between artificial neural network and random forest for effective disaggregation of building cooling load

Detailed monitoring of loads can provide sufficient information about buildings and help in improving the operation of energy systems. Non-intrusive load monitoring (NILM) has become popular owing to its low cost and lack of disturbance in the occupied space. In this study, NILM methods based on artificial neural network (ANN) and random forest (RF) are proposed and compared to obtain the sub-loads of cooling loads. The cooling loads are disaggregated into four sub-loads: occupant load, equipment load, fresh air load, and building envelope load. Results showed both NILM methods could achieve load disaggregation accurately, but the RF-based NILM method performs better than that based on ANN. Moreover, the RF-based NILM method has mean relative errors (MREs) between 2.5% and 13.0%, while the ANN-based NILM method has MREs between 3.1% and 12.9%. Among the four sub-loads, the equipment load could be disaggregated with the highest accuracy. The detailed sub-loads obtained using the proposed NILM methods can guide building renovation and optimization design of building energy systems.

Carbon footprint in building distributed energy system: An optimization-based feasibility analysis for potential emission reduction

Abstract Buildings account for more than one-third of the global primary energy consumption and are responsible for most of the global greenhouse gas emission. As one of the infrastructures of buildings, energy systems are crucial to control overall energy consumption and associated emissions. Despite much consideration on improving the efficiency of buildings energy system and the utilization of renewable energy, there has been little discussion on the carbon footprint quantification of building distributed energy systems as well as the reduction potentiality. In such a context, this paper proposed a life cycle economic and carbon emissions assessment framework by dividing the building distributed energy system into five stages. Subsequently, a multi-objective mathematical model is developed with minimal life cycle cost and carbon footprint, while respecting energy balance and technical constraints, and the optimal energy system and the carbon footprint distribution are obtained by solving the model. Finally, a case study is conducted on the building energy system design in a district in Xuzhou to test the effectiveness of this method. The correlation between optimal energy system carbon footprint by facility attributes, and the distribution of energy system carbon footprint by stage and source are investigated. It's found that the carbon emissions in the operation and maintenance stage accounts for the largest share in the entire life cycle. In addition, the proportion of carbon emissions from natural gas is the highest, accounting for 65–73% under environmental optimization and 85–88% under economic optimization. Followed by electricity, with a share of 26–34% and 10–14% under environmental and economic optimization, respectively. Finally, a sensitivity analysis is carried out to analyze the impact of energy carbon emission factors.

Method and Case Study of Multiobjective Optimization-Based Energy System Design to Minimize the Primary Energy Use and Initial Investment Cost

This study aimed to develop a building energy system design method to minimize the initial investment cost and primary energy use. As for the energy system, various combinations were generated depending on the type and capacity of the device used as well as the number of units, energy consumption, and efficiency of the building. Because the design process of energy systems is a critical step in determining the performance of the building throughout the lifecycle, an effective design method is necessary. The proposed method determines the energy system that can minimize the primary energy use and initial investment cost through a multiobjective optimization by calculating the cooling and heating energy consumptions of the building and initial investment cost of the energy system using the load profile by the design-day, and the information in the design phase of the building. This method can support the decision-making process by providing engineers with an alternative proposal for minimizing the initial investment cost and primary energy use by the Pareto analysis after reviewing the design combinations of various energy systems with limited information in the initial design phase. To verify the effectiveness of the methodology, a case study of the two buildings was performed, and the analysis results were compared to the conventional design alternatives. As shown in the case study results, using a method developed in comparison with the conventional result can provide the efficient alternative selection with 80% of initial investment cost and 86% of primary energy use, respectively. The results confirmed that the proposed methodology can provide various optimum results more effectively compared to the conventional design methods.

Comparison of building energy demand for hotels, hospitals, and offices in Korea

Energy demand characteristics were surveyed and measured throughout Korea to establish load models for three types of buildings: hotels, hospitals, and offices. The consumption of electricity and fuel was collected and statistically analysed to establish four types of building loads: electricity, heating, hot water, and cooling energy. The annual, monthly, daily, and hourly patterns of the four load types were derived for the three building types. Key statistical values such as the annual mean, maximum, and minimum values are provided. A procedure is presented to compose building load models as a time series covering the 8760 h of a year by combining daily and hourly patterns. The key features of the load models were compared to better understand the energy consumption characteristics of the three building types. Substantial variations in the magnitudes and patterns were observed among the types of buildings and loads. The load models can be applied to a wide range of problems in building energy system design and planning, including simulations and optimizations of community energy systems.

Sensitivity analysis for robust design of building energy systems

The comprehensive design of building systems incorporates the tasks of selection, sizing and control of devices. A simultaneous acquirement of these tasks is a necessity to achieve an overall optimal design. However, such mutual optimizations become a complex problem, implying a high computational effort. A greater challenge appears once the uncertainties of boundary conditions such as weather conditions, user demands and energy costs are taken into account. A common approach to protect the suggested system configuration against the possible uncertainties is a stochastic optimization which results in a robust design.In this paper, first the sensitivity of the design to selected boundary conditions is extensively investigated. In a second step, the resulting designs of the deterministic and stochastic optimizations are compared for several uncertainties. All optimizations are setup using a previously developed framework which is extended to solve the stochastic optimization problem. The comprehensive analyses show that the achievement of a robust design is computationally demanding and not even desirable in general. However, the size of devices may vary by up to 100% when a robust design is attained.

초기투자비와 1차 에너지소비량을 고려한 에너지시스템의 다중최적 설계 방법론

This paper proposed a multi-objective optimization method for building energy system design using primary energy consumption and initial cost. The designing of building energy systems is a complex task, because life cycle cost and efficiency of building are determined by decisions of engineer during the early stage of design. Therefore, methods such as pareto analysis that can generate various alternatives for decision making are necessary. In this study, the optimization is performed using the NSGAII and case study was carried out for feasibility of the proposed method. As a result, alternative solutions can be obtained for the optimal building energy system design.

Preliminary determination of optimal size for renewable energy resources in buildings using RETScreen

This paper describes a practical and simple method for determining the optimal size of renewable energy systems for building applications at the conceptual design stage. The method suggested in the paper is based on the use of the software tool RETScreen widely used for pre-feasibility or feasibility studies. The software is a convenient and relatively accurate tool for evaluating the feasibility of renewable systems. However, the tool does not provide with a method or function for optimal design. It seems there has been no publication or study to find optimal design parameters for renewable energy systems using the software. By using the features of this software, which can evaluate renewable technologies, this study provides a methodology to find the optimal design parameters for a renewable energy technology in the conceptual design of building energy systems. The paper describes a further development of the method for applications to integrated hybrid renewable energy resources in buildings. The paper also describes how to combine hybrid renewable heating systems, such as hybrid solar thermal and ground-source heat pump systems. Finally, this paper presents case studies on the optimal sizing of a single renewable system and an integrated renewable energy system for building applications.

Analysis of solar radiation measurements at Ghardaïa area, south Algeria

Abstract This study aims to explore the solar radiation field in southern Algerian peninsula, hourly measurements of global and diffuse radiant fluxes collected at Ghardaia site (32.360 N, 3.810 W, 450 m above MSL), from 1 January 2005 to 31 December 2008, are employed. These data were used to further addressing radiant fluxes distribution on hourly, daily, monthly and seasonal basis that in turn are of particular important from the point of view of proper design of building energy systems as well as accurate evaluation of thermal environment within buildings. Results obtained suggest that the annual value of clearness and diffuses indexes are respectively 0.68 and 0.37, and the monthly average daily values of global solar radiant flux exhibits seasonal variability with higher values in summer (7762 Wh/m2) and lower ones during winter (3915 Wh/m2). Regarding diffuse radiant flux, measurements shows that the monthly average daily value is about (1173 Wh/m2) in winter, while it is around (2829 Wh/m2) in summer.

IDABES: An expert system for the preliminary stages of conceptual design of building energy systems

This paper describes the development of an expert system which carries out the preliminary stages of the concept design of energy systems for buildings. The concept design is based on the optimal search of a space, which includes all possible solutions, using heuristic knowledge about the problem. Problem knowledge is divided into five categories: domain knowledge; constraint knowledge; procedural knowledge; analysis algorithms and solution knowledge. This knowledge has been represented in an expert system, written in OPS5 and accessing FORTRAN-coded analysis and graphics routines. Domain, constraint and procedural knowledge have been implemented in a hierarchical manner using production rules and linked data structures.