Achieve Energy Savings Research Articles

3D Numerical Modeling to Assess the Energy Performance of Solid–Solid Phase Change Materials in Glazing Systems

This research investigates the energy efficiency of a novel double-glazing system incorporating solid–solid phase change materials (SSPCMs), which offer significant advantages over traditional liquid–solid phase change materials. The primary objective of this study is to develop a 3D numerical model using the finite volume method, which will be followed by a parametric study under real climatic boundary conditions. A proposed double-glazing setup featuring a 2 mm layer of SSPCM applied on the inner glass pane within the air gap is modeled and analyzed. The simulations consider various transient temperatures and ranges of the SSPCM to evaluate the energy performance of the system under different weather conditions of Miami, FL during the coldest and hottest days of the year, both in sunny and cloudy conditions. The results demonstrate a notable improvement in energy performance compared to standard double-glazing windows (DGWs), with the most efficient SSPCM configuration exhibiting a phase transition temperature and range of 25 °C and 1 °C, respectively. This configuration achieved energy savings of 24%, 26%, and 23% during summer sunny, winter sunny, and winter cloudy days, respectively, relative to DGWs during cooling and heating degree hours. However, a 3% energy loss was observed during summer cloudy days. Overall, the findings of this study have shown the potential for energy savings by incorporating SSPCM with suitable thermophysical properties into double-glazing systems.

The Role of Building-Integrated Greenery Systems in Building Sustainability Rating Systems

Building rating systems allow for the evaluation of environmental buildings’ impact throughout their lifecycle, thereby enabling improved design. The integration of vegetation into building envelopes, through green roofs and facades, provides multiple benefits that enhance the sustainability of a built environment. In arid climates, Building-Integrated Greenery Systems (BIGSs) contribute to energy savings and the improvement of the urban environment through evaporative cooling. However, the maintenance of these green systems requires efficient water use. This study thoroughly reviews six selected building sustainability certifications to determine the extent to which BIGSs are considered in the certification process. The findings indicate that BIGSs are not yet well integrated directly into these certifications. While the certifications recognize the biophilic effects on users and contributions to sustainable construction, they often overlook scientifically proven benefits such as acoustic insulation and urban noise reduction. This study highlights the importance of updating certification frameworks to fully incorporate the diverse advantages of BIGSs, especially in enhancing indoor environments and achieving energy savings.

Weak magnetic effect and free radical reaction mechanism on the dissolution of lightweight magnesia in CaO–Al2O3–SiO2 melts

Lightweight magnesia-based refractories play a crucial role in achieving energy savings and carbon reduction in steelmaking furnaces. The primary requirement is to make lightweight magnesia refractories with high corrosion resistance. The introduction of a weak static magnetic field is also a promising way to improve the slag resistance of lightweight refractories in addition to optimizing the composition and structure of the refractories. In this paper, the dissolution behavior of the lightweight magnesia in CaO–Al2O3–SiO2 molten slags at and above 1600 °C under weak static magnetic field is presented. The influence of the weak static magnetic field on the dissolution rate was analyzed, the behavior from the perspective of free radical and kinetics was discussed, and the influence of different calcia–silica weight ratios (C/S) on slag, magnetic flux density and the reaction mechanism are revealed. The results show that the dissolution rate of the lightweight magnesia in the molten slag presents a significantly weak magnetic effect, with a reaction inhibition interval of 2–4 mT in the range of 0–16 mT magnetic field intensity, and the key inhibition threshold surpasses that of alumina. The mechanism of the free radical formation and reaction during magnesia dissolution in high-temperature slag is explained and illustrated. The relationship between the magnetic flux density, superoxide radical content, and dissolution rate is presented, providing theoretical guidance for developing magnetic combined magnesia refractories.

On-site evaluation of indoor thermal comfort and energy performance of a liquid-desiccant assisted air-conditioning system in a high-latent-load building

Indoor humidity control is increasingly important because indoor sensible heat ratio decreases to reduce building energy consumption. However, reference vapor compression cooling systems exhibit energy inefficiency and limitations in maintaining indoor thermal comfort. Therefore, this study proposes an alternative heat-pump-driven liquid-desiccant air-conditioning system, independently controlling air temperature and humidity. The reference and proposed systems are applied to a high-latent-load building to investigate their onsite indoor thermal comfort and energy performance empirically and simultaneously under various outdoor summer conditions. The reference system exhibits a thermal comfort satisfaction ratio of 97% only under limited hot and dry weather. Conversely, the thermal comfort satisfaction ratio sharply drops to 2% or less under humid weather. The proposed system consistently achieves a high thermal comfort satisfaction ratio exceeding 90% under various outdoor summer conditions. In empirical comparisons, the proposed system can maintain a thermally comfortable room for an hour while achieving energy savings exceeding 92.4% and 12.1% under warm and humid (rainy season) and hot and humid outdoor conditions, respectively. The proposed system is concluded to consistently maintain indoor thermal comfort while using energy efficiently, demonstrating its widespread applicability for general high-latent-load buildings characterized by low indoor sensible heat ratio values of 0.56 on average.

Energy efficiency in modular emergency shelters: Impact of envelope finishings and shadowing

In emergencies, ensuring rapid access to temporary housing for displaced individuals is a cornerstone of effective disaster response. Managing the issue time-effectively is crucial for addressing the immediate physical needs of the population and fostering a sense of stability. Unfortunately, given the vast scale of emergency camps, the number of shelters to be installed is often very high, requiring rapid transport and assembly for the success of settlement procedures. Furthermore, emergencies frequently occur in environments with extreme outdoor climates, heightening the challenge of maintaining indoor comfort within shelters. The present study considers a modular shelter based on composite sandwich panels made of glass-fibre reinforced polymer (GFRP) filled with a polyurethane (PUR) foam thermal insulation core. Experimental tests and numerical analyses are used to quantify the thermal conductivity of the panels and the heat losses through the panels' connections, respectively. Dynamic energy simulations are implemented accounting for the aforementioned data and used to quantify the energy required to ensure users' thermal comfort via active heating and cooling systems. Further, the model is applied to evaluate the influence of envelope finishes and shading devices in reducing the shelter's energy demands in three extreme climates. Results indicate that the combination of shading and reflective finishes can lead to significant energy demand reductions, decreasing the need for thermal insulation and, therefore, reducing the overall weight of the shelter. Finally, it is observed that in climates where cooling demands are dominant, passive strategies can achieve energy savings that thermal insulation alone cannot attain.

Predicting the PCM-incorporated building's performance using optimized linear kernel and tree-based machine learning methods

The devising of a precise machine learning-based energy consumption prediction model holds significant importance for taking effective decisions during the early stages of phase change material (PCM) incorporated building design to reduce energy consumption. Few researchers have developed prediction models for estimating the consumption of energy in PCM incorporated buildings. However, there is a need to develop the best-performing machine learning-based prediction model, which is less complex, involve a lower number of hyperparameters, and has a greater ability to generalize hidden patterns among the dependent and independent variables. To address the above shortcomings, the linear kernel and tree model-based prediction models were formulated for PCM-incorporated buildings located in eight cities of subtropical highland climate, considering the variations in their hyperparameters. The evaluation and validation of the formulated models for prediction were performed utilizing several statistical metrics. Test results substantiated that the ensemble-boosted tree-based prediction model (EBT20) exhibited superior efficacy in estimating the energy consumption for PCM-integrated building, having an average R2 > 0.93, NMBE <4 %, and CV-RMSE <8 %. The developed model showed less complexity and better generalization ability at the optimized values of ensemble boosted tree hyperparameters, which are as follows: minimum leaf size = 32, number of learners = 99, and learning rate = 0.1. From comprehensive evaluation conducted to evaluate the interpretability of the prediction model, it was found that the building orientation and PCMs melting temperature are the most influential parameters for achieving energy savings of up to 33 % in the selected climate zone.

Assessment of HVAC Performance and Savings in Office Buildings Using Data-Driven Method

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in building performance simulation software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. The findings indicate that by analyzing measured data and adjusting the configurable parameters, the energy consumption of buildings can be significantly reduced. The close monitoring of indoor parameters by building operators and making corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, this paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives.

Az energiahatékonyság szerepe a jelzáloghitelek csődvalószínűségében és a tőkekövetelmények meghatározásában

Our study aims to contribute to the literature on mortgage defaults and energy efficiency of the collateral. The risk differentials of green mortgages are highly relevant from a policy perspective, especially for the determination of capital requirements that are traditionally risk-based. As a theoretical background, we describe the channels through which the energy efficiency of households can reduce the probability of default. Within this decomposition, we distinguish between the effects of disposable income, financial literacy, the effect of wealth and the effect of the real estate collateral. We analyze the effect of the increased disposable income of energy-efficient households on the probability of default of mortgage loans in Hungary, where the central bank introduced a preferential capital requirement program for green mortgage loans. The primary outcome of energy efficiency gains is savings in energy units. In our study, we present the process of determining the theoretically and measurably achievable energy savings resulting from the limiting mechanisms. Due to the changing energy prices, we will not convert the energy measurement units into currency. The primary goal of our study is to present the methodology of how the results of energy savings can be included in credit risk.

Protocol-based state estimation of two-time-scale complex networks with nonhomogeneous sojourn probabilities

This paper is dedicated to researching the problem of the state estimation for two-time-scale complex networks (TTSCNs) subject to sojourn probabilities (SPs) under channel fadings and false data injection attacks (FDIAs). The network’s switching topology is governed by a novel switching law that incorporates time-varying SPs, with the variation of SPs being regulated through a persistent dwell-time (PDT) switching signal. Meanwhile, for the sake of achieving energy savings and mitigating data congestion, the dynamic probabilistic event-triggered weighted try-once-discard (DPEWTOD) policy is innovatively developed by integrating with the major merits of the dynamic probabilistic event-triggered mechanism (DPETM) and WTOD protocol (WTODP) based on hybrid compensation. Different from the traditional communication rules, this DPEWTOD concept not only captures stochastic behavior of the dynamic triggering threshold (DTT), but also enables a compensatory measurement that closely approximates the actual value. Subsequently, we construct time delay-, DTT-, and PDT-dependent Lyapunov functionals to analyze the stability of error dynamic systems in the presence of fading and malicious FDIAs. By employing a separation technique, our proposed solving algorithm transforms non-convex analysis conditions into tractable ones. Eventually, the rationality and effectiveness of the proposed method are demonstrated by an resistance–capacitance (RC) circuit example.

Generic load regulation strategy for enhancing energy efficiency of chiller plants

In many chiller plants, high coefficient of performance (COP) is only achieved at a few favorable part load ratios (PLRs), while the COP is low at many other non-favorable PLRs. To address this issue, this study proposes a generic load regulation strategy that aims to maintain chiller plants operating at high COP, particularly under non-favorable PLRs. This is achieved by incorporating thermal energy storage (TES) units and timely optimizing the charging and discharging power of the integrated TES units. The optimal charging and discharging power is determined by solving a dynamic optimization problem, taking into account the performance constraints of the TES units and the chiller plants. To provide an overview of the energy-saving potential of the proposed strategy, a comprehensive analysis was conducted, considering factors such as building load profiles, COP/PLR curves of chillers, and attributes of the TES units. The analysis revealed that the proposed load regulation strategy has the potential to achieve energy savings ranging from 5.7% to 10.8% for chiller plants with poor COPs under unfavorable PLRs, particularly in buildings with significant load variations.

Measuring the Decarbonisation Progress of Buildings Based on European Open Big Data

The European Commission (EC) has established climate neutrality by 2050 as one of its objectives. To achieve this, decarbonising the building stock is crucial since buildings are responsible for 36% of European greenhouse gas emissions. Monitoring the progress of decarbonisation is crucial to understand where we stand in terms of achieving long-term decarbonisation goals, facilitate evidence-based decision-making, promote accountability among member states (MS), and engage the public in these initiatives. However, data collection for monitoring the decarbonisation of buildings represents a challenge for MS, for which they may not be adequately prepared.To promote the collection and publication of open data, the EC has established frameworks for developing open data infrastructures within MS through various directives, offering comprehensive and well-organized information. Directives such as the Infrastructure for Spatial Information in Europe (INSPIRE), which advocates for the collection and accessibility of data in diverse topics, and the Energy Performance of Buildings Directive (EPBD), enabling the analysis of extensive information on the energy efficiency of constructed assets through the open publication of Energy Performance Certificates (EPC) databases, exemplify these efforts. However, this information often remains dispersed and narrowly focused, necessitating the integration of other data sources to extract meaningful insights.The paper delves into the benefits of georeferencing and automated cross-referencing of open data on buildings, enabling the monitoring of decarbonisation progress. To achieve this, we have developed a national-scale Urban Building Energy Model (UBEM) for Spain. This model is founded on EPCs and other open data derived from public information sources established through European directives. Additionally, we have investigated the prospect of integrating data from Digital Building Logbooks (DBL), which are emerging data repositories on buildings promoted by the EC in the forthcoming EPBD, into the UBEM to enhance the model.The study showcases significant potential in developing information on various topics, including characterising the Spanish building stock — a framework that may be extrapolated to other MS — in terms of energy performance based on location, archetype, and age. The study also focuses on estimating energy consumption and carbon dioxide emissions, monitoring the progress of renovations, evaluating the achieved energy savings, and identifying and characterising the least energy-efficient segments of the building stock.

Optimization of heat management and utilization in batch processes

Batch plants boast operational flexibility in manufacturing process and resource sharing, making batch processes suitable to fluctuating-demand or small-scale production. However, given time dependence of operational behavior, batch processes are more demanding in energy integration than continuous processes, a task which is especially important amid the global net zero-emission current. It is, therefore, imperative to develop sustainable design strategies for intermittent processes. The study introduced a targeting method and developed an innovative over-time heat integration technique to optimize energy utilization in batch processes, involving use of heat transfer media for heat recovery over various time intervals and a heat makeup strategy to heat the media over different periods, in order to enhance heat-energy utilization efficiency. The technique is applicable to both fixed and unspecified batch schedules, the latter of which involves simultaneous design of production schedule and heat recovery systems, increasing flexibility and improving heat energy utilization. Case studies are conducted to demonstrate the proposed method. With the heat makeup technique, energy efficiency can be significantly enhanced, achieving energy savings of 51% and 93.1% in hot and cold utility, respectively. Meanwhile, the simultaneous design of production schedule and heat integration can boost savings up to 53% and 99%. Results have proven the benefits of the technique, including significant reduction of energy consumption and carbon emission, contributing to clean production, environmental protection, and sustainable development.

Prediction and decision of free-piston linear generator on starting process for multi-fuel adaptability

Free-piston linear generators, due to their variable compression ratio, are considered to possess excellent multi-fuel adaptability. Further, fuel-flexible is a weighty research topic for addressing energy source limitations, meeting powertrain generalization requirements, and achieving energy savings and emission reductions. The initial concern to solve is the rapid starting of various fuels within the same free-piston linear generator. This study aims to introduce a mechanical resonance starting performance prediction model based on an artificial neural network and provides a control decision method based on single-objective optimization with soft constraints to achieve flexible starting of a variety of fuels. This paper also assessed the starting performance using the free-piston linear generator prototype and conducted a comprehensive parameterized analysis to investigate the influence of parameters and control variables on starting performance. Using the starting decision method proposed, the control variable values for ethanol starting were derived on the prototype, resulting in driving current at 3.5A, intake air mass flow at 95 standard liters per minute, and intake air temperature at 335 K. This decision value effectively met the predefined requirements, successfully igniting and starting the prototype with ethanol. This outcome serves as a robust validation of the fuel-flexible capabilities within free-piston linear generator systems.

The Application of Intelligent Architectural Design in Energy Saving and Sustainable Development

In today's context, energy conservation, emissions reduction, and sustainable development are highly prioritized, and the architectural industry must recognize the value of intelligent buildings to maintain stable growth amidst fierce competition. Intelligent design not only enhances the energy efficiency of buildings, achieving energy savings and emissions reductions, but also significantly improves their safety and convenience, providing a more comfortable living environment for residents. Moreover, it is a key force driving technological innovation in the architecture industry, leading it towards more efficient, environmentally friendly, and sustainable development. In terms of application principles, intelligent architectural design emphasizes coordination, integrity, and flexibility. It requires considering the harmonious unity between architecture and the environment, ecosystems, and human needs during the design process, treating the building as an integrated system, and focusing on the flexibility and adjustability of the design to accommodate potential future changes. In specific applications, intelligent designs for natural lighting, ventilation, and electrical systems are important manifestations of intelligent architectural design. These applications, through intelligent control systems and devices, achieve efficient management and adjustment of the building environment, thereby enhancing the building's energy efficiency and contributing to sustainable development.

Measured performance of energy efficiency measures for zero-energy retrofitting in residential buildings

Retrofitting existing buildings to Nearly Zero Energy Buildings (NZEBs) can significantly reduce their energy consumption and environmental impact. However, practitioners and policymakers still face the challenge of providing effective and economic energy efficiency measures (EEMs) to interested parties, due to the limited data on the actual performance of available EEMs. This study addresses the data deficiency by analyzing the actual energy and cost performance of a retrofitted single-family house towards a NZEB. Itemized electricity consumptions, including power consumption of heat pump, water pump, fan, and other accessories, as well as whole building usage, are measured before and after the renovation. New metrics are devised for effectiveness assessment – estimating energy savings upon degree day, cost and change in envelope thermal resistance R-value. The contribution of each EEM to the achieved energy savings is identified and compared. For energy consumption of air-water source heat pump, exterior wall retrofit yields the greatest savings: 1.02 kWh/dd (22.2 %) in winter and 0.64 kWh/dd (33.1 %) in summer, followed by roof retrofit. When the savings are analyzed in terms of per unit increment of R-value, windows/doors retrofit demonstrates the highest priority: 1.66 kWh/dd per unit increment of R-value in winter and 0.62 kWh/dd per unit increment of R-value in summer. Considering costs and energy saving contribution together, the optimal retrofit measure is roof retrofit. This study also contributes to the topic by adding new data to the field and extending retrofit practices to locations outside of North America and Europe.

Graphical pinch analysis-based method for heat exchanger networks retrofit of a residuum hydrogenation process

Sustainable energy systems are crucial for reducing carbon emissions because renewable energy sources leave a footprint. The petrochemical industry often suffers from inefficient heat exchange network (HEN) systems, leading to substantial energy wastage. In the current work, a real case study of the residue hydrogenation process was analyzed to identify potential energy savings. A new method combining Pinch Analysis and Thot–Tcold diagram analysis methods was proposed. This graphical analysis method plots the cold-flow temperature of each heat exchanger unit on the x-axis and the hot-flow temperature on the y-axis. By applying the Thot–Tcold diagram to a practical case of residue hydrogenation in Zhejiang, the existing process energy state was evaluated, and HEN was retrofitted to achieve energy savings and carbon emission reduction. Following optimization, the energy recovery amounted to 202.71 GJ/h with an energy recovery rate of 14.3 %. The proposed method saves approximately 4.058 × 105 GJ/y compared to current operations, resulting in an annual cost saving of approximately $ 2.76 M/y, with an investment payback period of less than 0.36 y. This study offers a solution to the energy challenges of industrial residue hydrogenation by enhancing the economic and environmental sustainability of existing process flows.

Online Energy-Aware Scheduling for Deadline-Constrained Applications in Distributed Heterogeneous Systems

In the current computing environment, the significance of distributed heterogeneous systems has gained prominence. The research on scheduling problems in distributed systems that consider energy consumption has garnered substantial attention due to its potential to enhance system stability, achieve energy savings, and contribute to environmental preservation. However, efficient scheduling in such systems necessitates not only the consideration of energy consumption but also the ability to adapt to the dynamic nature of the system. To tackle these challenges, we propose an online energy-aware scheduling algorithm for deadline-constrained applications in distributed heterogeneous systems, leveraging dynamic voltage and frequency scaling (DVFS) techniques. First, the algorithm models the continuously arriving applications and heterogeneous processors and proposes a novel task-sorting method to prioritize tasks, ensuring that more applications are completed within their respective deadlines. Second, the algorithm controls the selection range of processors based on the task’s subdeadline and assigns the task to the processor with the minimum energy consumption. Through experiments conducted with randomly generated applications, our approach consistently exhibits superior performance when compared to similar scheduling algorithms.

Impact of heating strategies towards energy-efficient buildings

Mitigating climate change and its impact requires intensive efforts to reduce greenhouse gas emissions, transition to renewable energy sources, and implement sustainable practices. Intermittent heating is considered a sustainable and effective way to achieve energy savings compared with continuous heating mode. However, recent studies have found that energy savings from intermittent heating depend on several factors, namely the building envelope solution. Therefore, this work intends to fill the research gap by comparing the energy efficiency and thermal performance of different construction systems, using two real-scale test cells, one built with a Light Steel Framing (LSF) solution and the other with concrete and Hollow Brick Masonry (HBM) solution. An experimental monitoring campaign was performed during the heating season, aimed at analysing the energy consumption, the responsiveness of the test cells to the heating system, and the influence of the outdoor climate on energy consumption, considering different heating strategies (intermittent patterns and continuous). In general, the LSF test cell registered lower energy consumption when compared to the HBM test cell. However, the LSF test cell presented significant fluctuations of the indoor temperature, confirming that it is more susceptible to the outdoor climate conditions. This research concluded that a system with lower thermal inertia has superior energy saving potential.

Improving Energy Efficiency of Carbon Capture Processes with Heat Pumps

Carbon capture based on chemical absorption in amine-based solvents is the most relevant technology option to reduce CO2 emissions from hard-to-abate industrial CO2 sources and power plants. Because of the energy consumption of the carbon capture process, which is mainly attributed to heating the desorber, carbon capture has a negative impact on the efficiency of the process where CO2 is removed. Heat pumps allow for heat recovery and provide high temperature process heat. Due to recent developments, heat pumps are now capable of supplying steam and providing high temperatures enabling new applications. This contribution investigates the integration of heat pumps into the carbon capture process of a furnace for steel processing to determine the most suitable heat sources and sinks and the achievable energy savings. The energy consumption of the carbon capture process can be lowered by 50% due to the integration of steam generating heat pumps. The cost analysis shows that the costs of steam have the highest influence on the costs of captured CO2.

HVAC energy consumption prediction based on RJITL deep neural network model

During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods.