Energy Performance Research Articles

Particle Swarm Optimization for multi-chiller system: Capacity configuration and load distribution

This study applies Particle Swarm Optimization (PSO) to enhance the energy efficiency of a multi-chiller system in a large office building, with a focus on optimizing capacity configuration and load distribution. Given the rising demand for sustainable energy solutions in buildings, where HVAC systems, particularly chillers, account for a significant portion of energy consumption, this research aims to reduce energy use and improve system performance. EnergyPlus simulations, based on Baltimore weather data and a reference large office model, were conducted to build a baseline dataset. This dataset was then used in Python to calculate energy consumption and optimize load distribution and capacity configuration. PSO was applied in three stages: optimizing capacity configuration using five built-in EnergyPlus algorithms, optimizing load distribution, and simultaneously optimizing both. The results showed that optimizing capacity configuration alone improved performance, even using traditional load distribution methods. Load distribution optimization outperformed other algorithms and converged at a 0.7 Part Load Ratio (PLR) for staging. The integrated PSO application achieved an 11.3 % reduction in energy consumption, a 21.5 % improvement in Coefficient of Performance (COP), and a 12.8 % increase in the Seasonal Energy Efficiency Ratio (SEER) by precise operation of 15 different combinations throughout the entire load range. These results demonstrate the potential of PSO to significantly enhance the efficiency of multi-chiller systems, providing a novel approach to optimizing both capacity configuration and load distribution. This research contributes to a robust framework for improving energy performance in building systems and offers valuable insights for future sustainable energy solutions.

Energy Efficiency of Educational Buildings Through Envelope Design Based on Field Observations and Sefaira Simulation (Case Study: FIP B Building)

Abstract The dense activity in the FIP B Building makes energy optimization in the building very important because it will impact faculty operations. This research shows deficiencies in the design of the existing FIP B building, which can be corrected through a number of aspects of change based on field observation and Sefaira Simulation that can increase the building’s energy efficiency in the form of adding skylights, creating an atrium, and increasing the window-to-wall ratio. The value of increasing the efficiency of the new model compared to the existing one is known by the Sefaira energy performance simulation for both model envelopes. The results of the simulation show that the greatest increase in efficiency is in the use of AHU fans, the second is in AHU cooling, and the lowest is in interior lighting and equipment. However, lighting usage has the potential to be more efficient with the relatively improved daylight accessibility in the new design model. The overall annual energy use and EUI level in the new model are slightly more efficient than the existing model. The result of this study provides aspects of consideration in the architectural design of campus buildings with large masses.

Numerical assessment of thermal bridging effects in 3D-printed foam concrete walls

Abstract Integrating smart technology and advanced materials in the construction industry has revolutionized traditional building practices, enhancing efficiency, sustainability, and overall performance. Researchers and professionals in the construction sector have shown significant interest in three-dimensional concrete printing (3DCP) for automating structural engineering tasks. Despite its potential as a sustainable solution to modern construction issues, there is a lack of research on the thermal insulation performance of three-dimensional printed concrete (3DPC) building envelopes, and the potential for integrating foam concrete (FC) to enhance energy efficiency has not yet been studied. This paper presents a numerical analysis examining how different infill geometries affect the thermal performance of 3D-printed foam concrete (3DPFC) lattice envelopes. Six lattice structures were designed with identical thickness, height, length, and comparable insulation areas. The effects of the contact (intersection) area of webs with the interior face shell, webs, and infill rows on the thermal performance of granularly insulated envelopes were studied. The effectiveness of insulation was also established. The findings indicate that the thermal transmittance of 3DPC envelopes correlates directly with the contact area of the webs and the interior surface, with U-values ranging from 0.151 W m2·K to 0.652 W/m2·K. Notably, the absence of direct connections between exterior and interior surfaces enhances insulation efficiency, with double-row structures achieving up to 94% insulation efficiency. However, when there is a direct connection between the two surfaces, the thermal performance of these envelopes is mainly affected by the contact (intersection) area of the webs with the interior face rather than the number of webs. By integrating foam concrete and double-row walls, this study demonstrates an innovative approach to reducing thermal bridging and improving energy performance in 3D-printed construction. The results offer novel insight into optimizing the thermal behavior of 3DPC systems for sustainable building practices.

Temperature adaptive passive daytime radiative cooling: The impact of their essential properties on energy performance of different building types

Passive daytime radiative cooling (PDRC) materials can reduce building cooling demands during hot periods but increase heating needs in colder times, known as heating penalties, limiting their practicality. Temperature adaptive (TA) materials offer a solution, yet optimal TA PDRC properties, such as changes in reflectivity or emissivity from high surface temperature (HST) to low surface temperature (LST) states and the switch temperature at which these changes occur, are under-researched. To address this, a wavelength-dependent PDRC model was coupled with EnergyPlus to identify the optimal reflectivity or emissivity in HST and LST states and determine the appropriate switch temperature. Results show that different reflectivity and emissivity values are needed for various climates to maximize TA PDRC benefits. Switch temperatures from 15 °C to 30 °C primarily affect cooling benefits with minimal impact on heating penalties. Additionally, the heating penalty and cooling benefit of TA PDRC vary across building types, requiring alignment between TA PDRC operational characteristics and HVAC systems to optimize advantages. Among building types, malls benefit the most from PDRC application.

Exergy analysis of process configurations for phenol recovery from wastewater

Wastewater with high phenol content, primarily generated by the petrochemical, coal, oil, and gas industries, must be treated before disposal to meet global regulations and prevent environmental harm. Liquid-liquid extraction is a well-studied method employed to address this need. Despite the extensive focus on to energy performance, exergy analysis has not yet been incorporated. Therefore, this study aims to compare four process configurations by integrating an exergetic approach with economic and emissions analyses to quantify thermodynamic efficiency, total annual cost, and CO2 emissions. Process simulations were conducted using Aspen Plus V.12, and the economic analysis was performed using the Aspen Process Economic Analyzer. The input streams of all configurations were 100 ton/h of wastewater containing 275 kg/h of phenol and 266 kg/h of hydroquinone, with pure methyl iso-butyl ketone (MIBK) as the makeup solvent. Extraction and mixing exhibited the lowest exergy destruction, while distillation accounted for approximately 55 % of the total exergy destruction. Configurations that incorporated direct medium-pressure steam input resulted in greater exergy destruction, leading to reduced exergy efficiency, but a lower total annual cost. The most economical configuration was achieved by heat integration of De Dietrich® process with low-pressure steam (C-IE lps), benefiting from exergy analysis insights. The exergy efficiency of this configuration increased from 33 % to 38 %, and the total annual cost decreased by 183 thousand USD per year compared to the initial De Dietrich® process (configuration C). Similarly, CO2 emissions were reduced to the lowest level, reaching 4.87 kg CO2 /h.

Strategic decision-making of competitive manufacturers: Assessing the role of product substitutability in energy performance contracting

Maximizing benefits through optimized strategic decisions is a crucial concern in Energy Performance Contracting (EPC) arrangements among competing manufacturers, yet this area remains underexplored. This paper develops a model to examine EPC cooperation between two manufacturers producing substitutable products in a duopoly market. By applying game theory, the study investigates the strategic decision-making processes of these manufacturers, with a particular focus on product substitutability. The analysis of equilibrium outcomes reveals that when both manufacturers engage in EPC, total market output increases. Although EPC results in lower prices, it enhances overall consumer surplus due to the price reduction. Additionally, EPC contributes to a decrease in total energy consumption as the cost of energy-saving service rises. The study find that as product substitutability increases, the sharing ratio determined by the client in the EPC arrangement gradually decreases. Concurrently, the level of energy-saving service provided by the Energy Service Company (ESCO) increases. However, when the cost coefficient of energy-saving retrofitting services is relatively high and the sharing ratio falls within a certain range, the service level initially decreases before rising again. This research offers novel insights into the impact of product substitutability on EPC engagements between two competing manufacturers, providing valuable perspectives on strategic decision-making within a competitive market environment.

Hybrid building energy modeling method with parameterized prototype models and rapid calibration

Building energy models were widely used in building energy performance analysis and efficiency improvement, such as building demand response assessment and energy system optimization. However, building energy modeling was a time-consuming and complex process. Therefore, this paper proposed a hybrid building energy modeling method based on parameterized prototype models and rapid calibration. The main feature of the method was the rapid generation of target building energy models using the prototype building energy performance database. The parameterized prototype building energy model considered 13 uncertainty parameters. Based on the range of 13 uncertainty parameters, such as wall U-value and solar heat gain coefficient, 1000 simulations were performed by Monte Carlo sampling to generate the prototype building energy performance database. The target building energy model was quickly calibrated by learning from this database. It filtered the models that met the requirements based on the actual building measurement data. Based on this hybrid modeling method, the rapid modeling tool AutoBPS-Hybrid was developed in a ruby environment. Shopping mall buildings located in three different climate zones were selected as case studies for this study. The results showed that if only one building energy model meeting the percentage errors was needed, only 3–4 simulations were required. If it was necessary to match the real uncertainty parameter distributions, an average of about 53 simulations was needed. The building energy models were applied to plug load and lighting control in buildings. The shopping mall buildings in Harbin, Beijing and Chengdu could reduce energy consumption by 10.18–13.33 kWh/m2, 14.43–18.15 kWh/m2 and 11.54–14.69 kWh/m2 per year, respectively.

Building retrofitting towards net zero energy under climate change

Abstract The challenge in Net-Zero Energy Building (NZEB) retrofitting is to identify the most effective measures to address energy performance issues. This paper presents a machine learning model for optimizing retrofit measures to achieve NZEB under the influence of climate change. Specifically, the non-dominated sorting genetic algorithm (NSGA-III) minimizes energy consumption and the predicted percentage of dissatisfaction (PPD) while achieving a NZE balance, thereby obtaining the Pareto front. The Order of Preference by Similarity to Ideal Solution (TOPSIS) ranking technique is then applied to the Pareto front to obtain an optimal solution. Various passive energy retrofit measures are investigated, and renewable retrofit measures are employed to cover the required energy. This process is repeated for different time frames to consider the impact of climate change on selecting retrofit measures. The results clearly indicate that, for retrofitting a residential NZEB, higher insulation values are needed for future scenarios compared to the present scenario due to the effects of climate change. Using the future scenarios defined by the Shared Socioeconomic Pathways framework, a higher level of envelope insulation and renewable retrofit measures are required to achieve NZEB in the Sustainable Future scenario compared to the Fossil Fuel-dependent Future scenario, with increases of 35% and 50%, respectively.

Hydrokinetic energy site selection under high seasonality: The i-IHE index

A novel index is developed to select sites for hydrokinetic energy exploitation in coastal areas with high resource seasonality – the typical case being estuaries whose hydrodynamics are highly influenced by variable river discharges. The intra-annual Integrated Hydrokinetic Energy (i-IHE) index is constructed by means of a new penalty function that modifies the Integrated Hydrokinetic Energy (IHE) index. The i-IHE index thus defined is applied to a case study: the Miño Estuary (NW Iberian Peninsula), with seasonal variations of up to 1000% in the riverine discharge. The hydrodynamics are modelled by the state-of-the-art Delft3D-FLOW code using a high-resolution 2DH grid. The results confirm a strong variability in the resource, with time periods shorter than seasons. The results obtained for the penalty function throughout the Miño Estuary, with values ranging between 0.5 and 0.75, show the relevance of the i-IHE index in order to not overestimate the potential of coastal areas subject to large intra-annual variability. Compared to the conventional IHE index, the novel i-IHE index highlights smaller areas with greater energy potential. The larger exploitable energy in these areas, along with the lower costs and fewer restrictions for hydrokinetic energy operation, will be of interest to developers and stakeholders.

An energy analysis methodology for residential heat pump retrofits

The government has targets to upscale domestic heat pump retrofit as part of the strategy to decarbonise heat. There are significant barriers to uptake including installation cost together with a lack of expertise in predicting and reporting energy and carbon performance. As a result, end user confidence in heat pumps can be low. This paper presents analysis of two monitored domestic properties using high resolution data. The aim is to develop reliable prediction and in-use diagnostic tools to improve transparency and reliability of heat pump performance. This paper focuses on a method drawn from theory as set out in CIBSE TM41, and shows how real building heat loss coefficients can be determined from regression analysis over different time frame resolutions. Practical application This research aims to provide residential heat pump retrofit providers with cost effective methods for assessing the suitability of dwellings to accept heat pump retrofit solutions, and to extend these techniques for the effective monitoring and performance evaluation of heat pumps post installation.

Overview of Fuel Cell-Hybrid Power Sources Vehicle Technology: A Review

Today, the world suffers from excessive and unfair consumption of non-renewable energy sources, such as high rate of global pollution and global warming. Accordingly, fields of life in general and industry in particular, led by the automobile industry, tended to use clean and renewable energy in industry and consumption to reduce the negative impacts on the global environment. The automotive industry tended to produce electric cars that do not depend at all on traditional energy sources from fossil fuel derivatives. Accordingly, it was necessary to find alternative energy sources that achieve both goals: avoiding the negative impact on the envi-ronment, and producing sufficient energy to achieve the requirements of performance and efficiency from the use of electric cars to be a permanent alternative to traditional cars that run on fossil fuels. This scientific paper provides an overview of one of the versions of modern technology in the field of electric vehicles energy performance to provide the vehicle with energy continuously and the mechanism of control and management in this system. This paper studies the hybrid power sources technology in electric and hybrid cars that depend on a main power source, Fuel Cells (FC), and a secondary power sources, Battery and Ultracapacitor, in the vehicle. This paper presents a brief overview of this system, its components, their characteristics, the advantages of hybridization in this type of energy source with the working mechanism of the system, an overview of the control systems in this technology and a set of challenges facing this type technology and its future perspectives.

View Factors Approach for Bifacial Photovoltaic Array Modeling: Bifacial Gain Sensitivity Analysis

Abstract Bifacial modules are highly valued in the global photovoltaic market since they are able to receive sunlight from both sides and can generate up to 10–30% additional energy compared to monofacial ones. They are integrated into various sectors, including building and notably Agrivoltaics. In this work, a coupled optical–thermal–electrical model was developed in matlab to simulate the energy performance and bifacial gain for a ground-raised bifacial system. The model allows optimization as a function of the photovoltaic (PV) system and bifacial module characteristics. A fixed, south-facing bifacial PV arrays composed of three rows assumed installed in Agadir, Morocco, is considered. The optical model is based on an analytical determination of view factors using the cross-string rule. These enable the determination of irradiances received by both sides of modules, which are subsequently used to evaluate energy yield of the system. Model validation was carried out based on various statistical metrics by comparing our results with simulation results generated by various softwares. The comparison shows a very good agreement, notably with 3dbifacialvf (R2 = 1, root-mean-square error (RMSE) = 0.03 W/m2 and R2 = 0.96, RMSE = 12.4 W/m2) for front and rear side irradiance, respectively. The DC energy generated by the system differs by less than 1% compared to pvsyst results. Sensitivity analysis revealed that all system parameters, particularly ground albedo, positively affect the bifacial gain. The bifacial gain increased from 10.6% to 20.1% as the ground albedo increased from 0.25 to 0.5.

Return-Temperature Reduction at District Heating Systems: Focus on End-User Sites

This review presents a comprehensive examination of recent advancements and findings related to return-temperature reduction in District Heating (DH) systems, with a focus on enhancing overall system efficiency at end-user sites. The review categorizes and clarifies various return-temperature reduction techniques, emphasizing aspects such as building energy performance, heat emitters, thermostatic radiator valves, and substation units. One shall note that return temperature is not a parameter that can be directly controlled within a DH system; instead, it is influenced indirectly by adjusting various system parameters throughout the design, commissioning, operation, and control phases. Key insights include the direct impact of heat demand on return temperatures; the pivotal role of indoor heating systems in optimizing thermal energy use in relation to heat demand; the significance of thermostatic radiator valves in regulating heat output and maintaining low return temperatures; the advantages of ventilation radiators and add-on fans in enhancing radiator efficiency; the necessity for effective substation operation to improve system cooling capacity; and the critical role of operational control strategies in achieving optimal system performance. These findings underscore the need for integrated approaches in DH system design and operation to achieve lower return temperatures and improve overall system efficiency.

An improved morphological filtering and feature enhancement method for rolling bearing fault diagnosis

Abstract The defects-induced periodic pulse is one of the important indices for the characterization of bearing failure. To solve the problem that the weak impact features caused by the early fault of the rolling bearing are easily to be interfered with by noise and strong background signal and are difficult to extract, an improved morphological filtering method combined with the Teager energy operator (TEO) is proposed to extract weak shock features. Firstly, according to the correlation between the periodic pulse induced by defects and the Morlet wavelet, the Morlet wavelet is used as the model to construct the structural elements. Then, capturing the Pearson correlation coefficient of the structural elements and the original signal and the signal is filtered by the variable scale morphological filter after threshold screening. Finally, the TEO is used as the post-enhancement link to suppress the noise in the signal after morphological processing and further highlight the fault characteristics. Simulation signals, experimental signals, and field signals verify the effectiveness and robustness of the proposed method.

Fault Feature Extraction of Rolling Bearing Based on Maximum Second-Order Cyclostationarity Blind Deconvolution and CEEMD-Teager

The operation of rolling bearings is directly related to the reliability of the whole rotating machinery. If the rolling bearing failure cannot be diagnosed and repaired in time, it will probably lead to equipment shutdown. The feature extraction process is an important part of bearing fault diagnosis. Some existing feature extraction methods are sensitive to noise and interference, and cannot fully explore and characterize useful information in nonlinear and non-stationary signals in complex scenes, and sometimes even lose important information contained in the data, resulting in low diagnostic accuracy. Therefore, a new method combining Maximum Second-order Cyclostationarity Blind Deconvolution (CYCBD) and Complementary Ensemble Empirical Mode Decomposition (CEEMD) was proposed for feature extraction of rolling bearing faults. Firstly, the cyclic frequency is set according to the failure frequency, and the original signal is filtered by CYCBD. Then, the filtered signal is decomposed into multiple Intrinsic Mode Function (IMF) by CEEMD, and the effective IMF components are selected by kurtosis criterion for reconstruction. Finally, the Teager energy operator is used to enhance the transient impact of the reconstructed signals. The results of simulation and comparison experiments show that the proposed method can extract bearing characteristics in different fault states more effectively, and the accuracy of network model diagnosis is improved compared with the traditional methods.

An architectural design framework to promote healthy indoor-outdoor connections in Arctic housing

This research proposes biophilic intermediate spaces as a promising architectural solution to improve indoor-outdoor connections, occupant well-being, and energy efficiency in Arctic housing. Basic examples of intermediate spaces in Arctic housing models include porches and vestibules. However, the architecture of these spaces has not yet been optimized to adress extreme climatic conditions and occupants’ needs. Therefore, this research develops an architectural design framework to optimize the architecture of intermediate spaces for Arctic housing to meet occupants’ well-being needs and improve housing energy efficiency. The research methodology combines an archetypal approach, exploratory case study analysis, and scoping literature review. The archetypal study examines the historical development of Canadian Arctic housing models in Nunavut, revealing the typological evolution, features, potentials, and deficiencies of intermediate spaces in the Arctic. The study identifies of the main design variables and performance indicators of intermediate spaces corresponding to healthy and positive indoor-outdoor connections, thermal and visual comfort, and energy efficiency. A conceptual model of an intermediate space is then simulated as an exploratory case study for a public Canadian Arctic residential building. The architectural design framework is established based on findings from the literature, archetypal studies of Arctic housing, and simulation results of an integrated model for an intermediate space and a public Arctic housing model in Nunavut. The proposed framework includes main architectural variables such as physical adjacency, material, orientation, space depth, and transparency ratio which impact thermal, lighting, and energy performance. This design framework can serve as a reference for creating policies and decision-making processes that integrate biophilic intermediate spaces with Arctic building practices, contributing to Canada's strategic plan for energy efficiency and vegetable production in the Arctic.

Energy consumption analysis and operating characteristics research on small time scale of variable refrigerant flow air conditioning systems in public buildings

In order to analyze the energy consumption and operation characteristics of the variable refrigerant flow (VRF) air-conditioning system on small time scale and put forward effective energy-saving suggestions, the VRF system operation experiments are carried out under different indoor set temperatures, different indoor unit opening quantities and different indoor set air velocities. This experiment studies the main influencing factors of energy consumption and operation of the VRF system, such as indoor temperature difference, indoor air velocity, outdoor environment temperature and so on. The results show that the rated power of indoor unit is linearly related to the energy consumption of the VRF system. The total active power changes greatly when the VRF starts and stops, and the outdoor ambient temperature changes. The indoor set air velocities have a great influence on the energy consumption and operation of the VRF system. The performance parameters increase first and then decrease with the increase of compressor frequency and load rate. When the load rate is 40 %∼60 %, the performance parameters can reach 3.2 ∼ 4.2.

Energy Efficiency and Sustainability in Food Retail Buildings: Introducing a Novel Assessment Framework

One of the primary global objectives is to decrease building energy consumption to promote energy efficiency and environmental sustainability. The large-scale food retail trade sector accounts for over 15% of total primary energy consumption in Europe, posing a significant challenge to the transition towards green energy. This study proposes a simple method for energy efficiency, environmental sustainability, and cost-saving assessment and improvement in large-scale food retail trade buildings. It aims to analyze the energy and environmental performance of building–plant systems, establishing an interactive network to assess intervention potential for the energy transition. The investigation focuses on the proper selection and analysis of the benefits of retrofit solution implementation, emphasizing potential energy savings in current and future climate change scenarios. Dynamic simulation with the Building Energy Model (BEM) was used to evaluate the impacts of building–plant system retrofit solutions, such as high thermal insulation, photovoltaic (PV) panels, Light Emitting Diode (LED) installation, waste heat recovery, and improvement in refrigeration units. The results show a reduction in annual energy consumption for the PV panel installation by up to 29% and lighting systems with high-quality LED to 60%. Additionally, CO2 emissions can be decreased by up to 41% by combining these two strategies.

ANALYSING THE EFFICIENCY OF USING POROUS METALS IN HEAT EXCHANGERS

This study focuses on analysing the effectiveness of porous metals in heat exchangers, focusing on their effect on improving heat transfer processes. Porous metals, due to their unique structure, have high thermal conductivity and increased contact surface area, which contributes to more efficient heat transfer. As part of the study, experiments and theoretical analyses were carried out to evaluate the effect of porosity coefficient on heat exchange performance. The plotted dependence of Freon mass flow rate and heat quantity on porosity coefficient demonstrate the linear nature of these dependencies. Mathematical processing of the data showed that an increase in the porosity coefficient leads to an increase in both the amount of heat transferred by Freon and Freon mass flow rate, indicating an increase in heat exchange efficiency. The results confirmed that porous metals can significantly improve the performance of heat exchangers and energy saving. Based on the study, further investigation and implementation of porous metals in heat exchangers is recommended to optimise their performance and improve the overall system efficiency. These results open new perspectives for the application of porous metals in various industries including power and mechanical engineering.

Energy system optimization based on fuzzy decision support system and unstructured data

To address the complex challenges in energy systems, this study proposes a novel optimization framework that integrates fuzzy decision support and unstructured data processing technologies. This framework aims to improve efficiency, reduce costs, decrease environmental impact, increase system flexibility, and enhance user satisfaction, thereby promoting sustainable development in the energy industry. The framework combines the innovative Energy Semantic Mapping Model (ESMM) and the advanced deep learning architecture ResNet to process textual and visual data effectively. ESMM enables accurate prediction of energy demand, while ResNet significantly reduces equipment maintenance costs and improves energy distribution efficiency. These advancements are critical as they address the limitations of existing approaches in handling large-scale unstructured data and making informed decisions under uncertainty. The Environmental Impact Assessment (EIA) confirms the model's effectiveness in reducing carbon emissions. A comprehensive economic analysis demonstrates substantial cost savings in energy procurement and operations and maintenance, with overall savings exceeding 25%. Enhanced user satisfaction and reduced system response times further validate the practical utility of the proposed approach. Additionally, a genetic algorithm is used to optimize the fuzzy rule base, enhancing the robustness and adaptability of the model. Experimental results show superior performance compared to traditional systems, providing strong empirical evidence for the intelligent transformation of energy systems. This research contributes to the field by offering a more sophisticated and flexible solution for managing energy systems, particularly in terms of leveraging unstructured data and improving decision-making processes.