Energy Efficiency Measures Research Articles

Assessment of Retrofitting Old Residential Buildings in Urban Districts: Expected Performance of Selected Energy Efficiency Measures

Assessment of Retrofitting Old Residential Buildings in Urban Districts: Expected Performance of Selected Energy Efficiency Measures

Energy efficiency measures in existing buildings by a multiple-objective optimization with a solar panel system using Marine Predators Optimization Algorithm

To enhance the energy effectiveness of old buildings, retrofitting strategies are an efficient technique. The objective here is to apply a multiple-criteria optimization method to retrofit the building envelope. In this respect, a new metaheuristic algorithm, called Marine Predators Optimization Algorithm (MPOA) is used in this paper. The components that are investigated to be retrofitted are including roofs, windows, and outer walls. A photovoltaic system is considered to be employed on the rooftop. The degradation duration of the solar panels is studied. Also, for renewable energy development and its long-lasting impact on the retrofitting scheme, a maintenance design is considered. The optimum utilization of the financial investment for the maximization of energy savings and economic-related advantages are the main targets. Specifically, the major operation indicators of the retrofitting scheme are energy savings, payback period, and net present worth. A weighted sum technique is applied to solve the non-linear problem. The efficiency of the suggested model is a result of the designed retrofitting plan.

Does recalling energy efficiency measures reduce subsequent climate-friendly behavior? An experimental study of moral licensing rebound effects

Energy efficiency measures are a key element in the fight against climate change. However, their effectiveness is threatened by so-called rebound effects—behavioral changes that lead to increased use and thus to lower-than-expected absolute savings. The moral licensing bias might cause rebound effects on the individual level: When people implement energy efficiency measures, they may feel that they have done something good for the climate and thus license themselves to less climate-friendly behavior later on. We test this prediction with three pre-registered experiments (total n = 2315). While our manipulated efficiency does increase the feeling of having done something good for the climate, no negative effects emerge on subsequent climate-friendly behavior—contrary to the predictions derived from the moral licensing literature. Climate-friendly behavior is thus not hampered by prior energy efficiency behavior; rather, demographics (women and older people act in a more climate-friendly manner) and environmental attitude determine it. Our results imply that the literature suggesting moral licensing is probably not transferable to the field of energy efficiency, and thus moral licensing may not lead to rebound effects in this context. We discuss possible explanations, implications for climate policy making, and needs for future research.

Wall and air conditioner combination for the best energy and economic performance: Methodology demonstration for high-rise residential buildings

Enhancing energy efficiency of any system with affordable measures is always a ‘win-win’ option, specifically for systems consuming large amount of energy. With increasing population and economic growth, rapid urbanization is inevitable, more so in developing countries. Due to higher population density and scarcity of available land, high-rise residential and commercial buildings are now increasing in big cities. Building energy efficiency has eventually emerged as a critical issue for such high-rise buildings. Space cooling consumes a huge amount of energy for high-rise buildings in tropical countries. Selection of masonry materials for exterior walls with matching air conditioners should be optimized for the best energy efficiency vis-à-vis least cost for high-rise buildings. Such a study to determine the optimized solution is reported using data of ten typical high-rise residential buildings in the warm-humid climate of Kolkata, India. The methodology followed is an integrated iterative process involving whole building energy simulation to determine the optimum performance out of different possible combinations of building walls (i.e., passive measure) and selection of corresponding air conditioners (i.e., active measure) for simultaneous best energy efficiency and minimum costing. Results show that the combination of autoclaved aerated concrete block wall and 5-star split air conditioner emerges as the optimum, being 37.53% more energy efficient and 24.27% more cost effective than the typically practiced baseline option. The required 29.47% higher capital investment is well compensated for lower annual expenditure for operational energy when fifteen years’ cash flow is considered. The study shows an integrated methodology for deciding the optimum combination of passive and active measures for the possible best energy performance at a minimum cost at the early design stage of high-rise buildings.

Urban polycentrism and total-factor energy efficiency: An analysis based on the night light data

Improving energy efficiency is crucial for environmental protection and resource conservation. Considerable urban system distribution is vital to realizing high energy efficiency. However, studies on energy efficiency from this spatial perspective remain limited. Using the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) and visible infrared imaging radiometer suite (VIIRS) nighttime light data, we measured urban polycentrism (UPC) and employed improved stochastic frontier analysis (SFA) for total-factor energy efficiency (TEE) measurement. Analyzing panel data from 332 prefecture-level cities across 27 provinces of China, we used the spatial economic and threshold models to calculate UPC's impact on TEE and its threshold conditions. The results revealed UPC's effect on TEE and the control variables (economic development, urbanization rate, and industrial structure). The pattern suggests that only when economic development, urbanization rate, and industrial structure reach a certain (range) level in some zones, does the effect change from one condition (inhibition/promotion) to another (promotion/inhibition). Under certain conditions, a win-win situation between UPC and TEE is achieved. Finally, relevant policy recommendations for energy efficiency improvements based on urbanization are discussed. To the best of our knowledge, this is the first study to systematically investigate the impact of UPC on TEE at the city level.

Data-driven prediction of ship fuel oil consumption based on machine learning models considering meteorological factors

To improve the energy efficiency of ships and reduce greenhouse gas (GHG) emissions, the implementation of energy-efficient operation measures is particularly important. Driven by this, this study was dedicated to improving the accuracy of ship fuel oil consumption (FOC) prediction and laying the foundation for optimizing energy-efficient operations. Firstly, we combined voyage reports and meteorological data and constructed six datasets containing different features. These features comprise navigation-related features encompassing sailing speed, displacement and trim, as well as meteorological features encompassing wind, wave, sea current, sea water salinity and sea water temperature. Secondly, we conducted experiments with 14 popular ML models on the datasets and compared the prediction performance of different models by a new scoring system. Finally, we explored the advantages and disadvantages of each dataset based on the model performance scoring results and analyzed the effects of related meteorological factors on FOC during navigation. The key findings of the proposed work were that extra trees (ET), random forest (RF), XGBoost, and LightGBM had good fitting and generalization performance. Set5, the dataset containing the most complete meteorological data, achieved the best prediction results. In particular, it had an R2 (test) of 0.9317 on the ET model, which was 1.97% higher than the R2 (test) of the dataset using only voyage reports. The conclusions can assist shipping companies in constructing a ship FOC prediction framework and developing ship fuel-saving strategies.

Cracking the code: mapping residential building energy performance in rural Central Asia through building typologies

Buildings in rural Central Asia have unique characteristics as they were built during the Soviet era (during 1950–1960) without any energy efficiency measures. The special and aged building stock pose a crucial challenge on energy security and energy supply. However, accurate scientific data on their energy usage is lacking, highlighting a research gap about robust and validated methodology to determine the energy use of rural Central Asian buildings. In response to this need, this research paper proposes a set of generalised residential building typologies for rural Central Asia, with a focus on Kyrgyzstan. The study combines quantitative and qualitative methods, including household surveys, energy demand modelling, EnergyPlus simulations, and result validation. It derives energy-based building typologies from construction year and building envelope characteristics. Through numerous validated simulations, it was identified that the heat demand for rural Kyrgyz houses is 3–5 times here compared to European houses (250–400 kWh/m2). The study revealed the estimated heat demand for homes in rural Kyrgyzstan, indicating a significant potential for improving building energy efficiency in the region. These results can inform specific approaches, such as building renovation plans, energy certification, and renovation strategies. Furthermore, the validated methodology introduced offers opportunities for researchers in the field of building energy efficiency and can be applied to similar regions in Central Asia with comparable building stocks and climates.

Towards Efficient Energy Usage at Ain Shams University Campus

In the light of global energy transition to renewable resources and energy efficiency usage, Ain Shams University (ASU) developed an ambitious plan to transform its campus into Green Campus. From an energy perspective, energy consumption data were continuously collected and audited to calculate the university campus carbon footprint. An energy usage strategy was established to tackle various pillars such as electrifying the campuses’ transportation system, improving energy efficiency usage, generating Renewable Energy (RE) for self-consumption, etc. Extensive research has been initiated on electric vehicles, wind and solar Photovoltaic (PV) energy generation with students’ activities/competitions. Thus, electric cars and buses were manufactured at the Faculty of Engineering (FoE) for elderly people and staff movement in ASU campus. Solar PV lighting poles with batteries were installed in the main campus. A small-scale Wind Turbine (WT) is manufactured and installed at the FoE and a pilot solar PV system is installed as well. Currently, an energy efficiency project is under implementation in various buildings/faculties and a parking lot that targets energy efficiency and solar PV energy generation. An energy efficiency measure is under implementation through replacing lamps with LED lamps, installing motion sensors, setting up a control center for monitoring and operation that is supported by Artificial Intelligence decision making algorithms. Rooftop solar PV energy systems are under design with smart meters. The project is targeting energy saving and bill reduction by at least 30% and as a result a reduction of carbon footprint will be achieved following the COP27 recommendations.

Energy Consumption Analysis, Efficiency Measures and Renewable Energy Investments Towards a Nearly Net-Zero Campus: The Case Study of Cyprus International University

According to the International Energy Agency, energy and process emissions from buildings are responsible for nearly 33 % of global CO2 emissions in 2021; with two other leading sectors; transportation and industry with emissions of 22 % and 30 %, respectively. As a result, parallel with global signs of progress, the topic of energy efficiency and energy management in buildings has come to be a rule of energy policy in Cyprus International University (CIU) buildings. In Cyprus, similar to the reference of IEA, nearly 30% of the energy consumption is from the buildings, which is the most open to energy-saving measures. Since 2017, Sustainable Energy Research Centre in CIU has been working on the energy measures of CIU on both supply and demand sides. To increase the university’s sustainability further in the Energy and Climate section of both UI GreenMetrics and contribution to UN Sustainable Development Goals, conscious energy steps are taken both in energy saving and alternative renewable energy sources in the whole campus which resulted in making CIU a leading example of a sustainable university campus in the Middle-East as it holds the 3rd place for the most sustainable university campus in the region according to UI GreenMetrics World Sustainable University Ranking in 2022. In this paper, an energy audit study will be presented which evaluates the total energy load and energy-saving potential on campus and propose measures that should be taken. In order to conduct this study, Education and Humanities (EH) Building is taken as the pilot study which considers a detailed audit and actions to minimize energy consumption. Then the findings obtained from this energy efficiency study are generalized to all existing buildings on the campus and the expected effect of energy efficiency attempts is projected. These results are joined with the future alternative energy projects planned on the campus and the roadmap to nearly net-zero campus is discussed at the end of the study.

Exergy efficiency and EEXI analysis of a marine power plant at partial load conditions

Energy efficiency and environmental protection are becoming more important in the world. Almost 90% of the world trade is carried by sea which makes energy efficiency very important for the maritime sector. Exergetic method is a powerful scientific tool for measuring energy efficiency. The ships are not always cruising at maximum load conditions. The situations such as narrow water passages, maneuvering, and, hoteling etc. are partial load conditions and the exergy loss in power systems reaches maximum values at these conditions. Besides the new IMO regulation forces the sector to reduce emissions through Energy Efficiency Existing Ship Index (EEXI) and one of the most popular solutions is Engine Power Limitation (EPL). In the literature there are limited studies investigating exergy efficiencies of partial load conditions of a marine power plant. In this article, the performance analyses of a combined power system in partial load conditions are carried out using exergy method. The considered partial load conditions are maximum-ahead, full-ahead, half-ahead, slow-ahead and dead-slow-ahead loads of the engine. The results show that, the exergy efficiency of the overall system is decreasing at lower load conditions. It is observed that the maximum exergy efficiency is 51.6% which is reached at 96 rpm. The best condition considering both exergy efficiency and EEXI is 80 rpm of main engine. At slow-ahead and dead-slow-ahead conditions the exergy efficiency decreases to about 33%.

A study on the prediction of electricity consumption considering the energy efficiency measures—applied in case of the Brazilian public sector

A study on the prediction of electricity consumption considering the energy efficiency measures—applied in case of the Brazilian public sector

Analysis of Barriers to Green Data Centers Implementation in Malaysia, using Interpretive Structural Modelling (ISM)

The data center market is expected to grow at 7% during 2022- 2027 and the market size is to reach RM2.0 billion in 2027. The high impact of ICT contributes to high energy consumption, which will impact the environment and greenhouse gas emissions. To reduce energy consumption is to introduce a green data centre. Implementing a green data center involves the introduction of energy-efficient measures for the data center. This will include using energy-efficient ICT equipment and energy-efficient facilities equipment, especially the HVAC system. The successful introduction of the green data center involves new technologies in which there will be barriers that need to be addressed. Based on the study, green awareness and the benefit of switching to green data centers is essential for green data center initiatives in Malaysia.

Developing a standardized categorization system for energy efficiency measures (1836-RP)

The growth of legislation to reduce energy use in existing buildings is producing a rich new trove of data about energy efficiency measures (EEMs), which has the potential to unlock new insights into the built environment. However, the lack of standardized EEM naming conventions and categorization methods is currently a major barrier to aggregating and analyzing this data. The goal of this study was to develop and test a novel standardized system for categorizing EEMs. The system consists of two components: a three-level building element-based categorization hierarchy, and a set of measure name tags, which are used to label an EEM and categorize it on the hierarchy. A demonstration and testing process was developed and applied to two sample datasets to evaluate the ability of the system to categorize a variety of EEMs. The results show that most EEMs can easily be categorized manually according to the new system, and highlight several challenges for automated categorization, including EEM names that are missing an element, contain a term not in the tag list, or contain synonyms or abbreviations. These results provide a replicable and systematic framework for the translation, aggregation, and analysis of EEM datasets from different sources.

A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data

Demand-side management (DSM) is crucial to smart energy systems. This paper presents a data-driven approach for understanding the relationship between energy consumption patterns and household characteristics to better provide DSM services. The proposed method uses a robust learning fuzzy c-Means clustering algorithm to automatically generate the optimal number of customer groups for DSM, and then uses symmetric uncertainty techniques to identify the identified load patterns and socio-demographic characteristics as the features for training a deep learning model. The model obtained can be used to predict the possibility of DSM group membership for a given household. This approach can be applied even in situations where smart meter data are not available, such as when new customers are added to the system or when residents change, or due to privacy concerns. The proposed model is evaluated comprehensively, including prediction accuracy, comparison with other baselines, and case studies for DSM. The results demonstrate the usefulness of weekly energy consumption data and associated household socio-demographic information for distinguishing between different consumer groups, the effectiveness of the proposed model, and the potential for targeted DSM strategies such as time-of-use pricing, energy efficiency measures, and demand response programs.

Assessing the Cost-Effectiveness of Incentives for Energy Transition Using Marginal Abatement Cost Curves

Incentive policies play a crucial role in encouraging the adoption of renewable energy sources and energy efficiency measures. This study analyzes and compares the incentives for energy transition in the South Tyrol region of Italy using a marginal abatement cost (MAC) curve constructed with an expert-based approach. The incentives for residential energy efficiency, mobility, and boiler replacement are characterized based on assumptions for costs, energy savings, and parameters. The resulting expert-based MAC curve analysis reveals boiler replacement incentives to be the most cost-effective, yielding CO2 reductions at the lowest cost but with limited potential as the incentive is limited to apartment blocks that are not in district heating areas. Mobility incentives enabling electric vehicle adoption have the highest CO2 reduction potential, albeit at higher costs per ton abated. Residential energy efficiency incentives fall between the two for cost-effectiveness and potential. The MAC curve approach provides a useful comparison of cost-effectiveness versus potential, guiding policy prioritization. This techno-economic assessment methodology can be applied to other regions pursuing energy transition. Overall, a balanced policy mix encompassing transport, buildings, and heating is required for comprehensive low-carbon transition.

Solar neighborhoods: the impact of urban layout on a large-scale solar strategies application

The article addresses the challenges of evaluating energy performance in different neighborhood settings under various energy efficiency measures and proposes a methodology for selecting appropriate solar strategies on a neighborhood scale. The study selects five representative neighborhoods from various climatic zones with different building and street layouts. The proposed methodology involves a systematic three-step multi-domain workflow for implementing energy efficiency measures and solar strategies in the existing neighborhoods. The first step involves typical energy performance simulation, the second step involves energy simulation using high performance building envelope, and the third step involves the addition of solar strategies in combination with retrofitting materials to achieve net-zero status. The results of the study show that modifying the building envelope leads to a significant reduction in energy consumption, with up to 60% reduction observed. The study also finds that the optimal mix of solar strategies depends strongly on the type of neighborhood, its street layouts, and the type of buildings. The article highlights the importance of considering these factors when implementing solar strategies on a neighborhood scale to achieve energy efficiency and net-zero status. It provides urban planners with a systematic decision-making approach to evaluate and optimize neighborhoods to achieve net-zero energy status.

Review of Industrial Heating and Potential Low-carbon Fuels in the Context of Nepal.

Process heating is an important step in manufacturing industries such as metal, cement, brick, food, etc., and accounts for a large share of the total energy consumption of those industries. In Nepal, process heating alone accounts for around 64% of the process energy of the manufacturing sector. Around 64% of Nepalese industries’ energy demand for process heating is supplied by high-carbon emitting fossil fuels such as coal, diesel, etc. The industrial sector is responsible for significant global greenhouse gas emissions. Nepalese manufacturing Industries emit around 0.002248 GT of CO2 annually. The movement toward carbon neutrality has prompted many countries around the world to look for clean and sustainable fuel to meet their decarbonization goal. This study overviews Nepalese industries’ different industrial heating processes in terms of energy consumption, emissions, energy efficiency measures and interventions, and renewable options from a literature review and using data from secondary sources. Based on the data collected and the literature review of the industries, the potential low-carbon fuels which can be compatible with heating applications in Nepalese industries are reviewed. This paper concludes with recommendations for the future decarbonization of process heating in Nepalese industries.

Towards healthy and energy-efficient buildings in the context of Egypt: Modelling demand-controlled ventilation to improve the indoor air quality in a generic office space in Cairo

Cairo is characterised by high concentrations of ambient air pollutants, especially air particulate matter of diameter less than 2.5 micrometre (PM2.5). Many studies have emphasized the impact of PM2.5 on people’s health and wellbeing including a World Bank report that has attributed 12% of the total annual deaths in Cairo to the exposure to ambient PM2.5. On one hand. improving the energy efficiency of buildings may involve implementing energy efficiency measures that aim to achieve indoor thermal comfort by maximizing the use of natural ventilation and minimizing mechanical air-conditioning. However, while natural ventilation can help reduce CO2 levels, it can also potentially lead to an increase in indoor PM2.5 levels. This study aims to investigate the impact of multiple air filtration scenarios on the energy consumption and the indoor air quality for a shoebox model that aims to represent generic offices Cairo. The study uses EnergyPlus simulations that leverage an Energy Management System script to model the demand-controlled ventilation, apply air filters when required, and simulate the increase in energy use due to the relevant pressure drops in the air system. The results for the scenarios investigated in the study highlighted that air filters can reduce the average indoor PM2.5 levels by nearly 40% during occupancy hours while causing an estimated increase of around 2-7% in the total operational energy. Given data and assumptions relevant to the study context, it was found that filtering the recirculated air while minimizing the introduction outdoor fresh air can be sufficient to minimize indoor PM2.5 levels.

Exploring assumptions for air infiltration rate estimates using indoor radon in UK homes

Radon, a known carcinogen, is one of the most commonly monitored indoor contaminants. This paper utilises findings from a previous study on indoor radon measurements in United Kingdom (UK) homes to explore the UK Government’s Standard Assessment Procedure (SAP) assumptions for air infiltration rates. These assumptions are important as they are used to assess the energy performance of dwellings and compliance with building regulations. Indoor radon data is aggregated by 16 combinations of home energy efficiency measures (loft and wall insulation, glazing upgrades and draught proofing) and fitted using a simple analytic radon model. We find indoor radon to be inversely proportional to air change rate and proportional to a fit coefficient, k, of 42.2 ± 3.1 (95% Confidence Interval (CI)). We also show that the assumptions within SAP used to estimate home infiltration rates can be modified to include the impact of home energy efficiency which improves the fit (R 2 from 0.38 to 0.51) to the radon data. This work provides evidence to help improve assumptions regarding the effects of home energy efficiency on infiltration rates.

The future demand of renewable fuels in Germany: Understanding the impact of electrification levels and socio-economic developments

The Climate Change Act recently enacted in Germany highlights the urgency of understanding the future demand for renewable fuels. In this study, we combine technological progress and socio-economic pathways in an energy system analysis to assess future renewable fuel demands in Germany. We apply the whole-system optimisation model, TIMES, to investigate transition pathways with varying electrification levels and socio-economic developments. The results show that renewable fuels demand varies between 388 PJ and 1310 PJ depending on the electrification rates. Furthermore, our findings demonstrate that considering socio-economic aspects and behavioural change, as represented by different Shared-Socio-economic Pathways, can significantly alter the demand for renewable fuels within a narrower yet still noteworthy range compared to the electrification scenarios. This provides country-level evidence highlighting the often-overlooked influence of social developments on demand projections. Consequently, it becomes crucial to prioritize the consideration of the climate mitigation potential arising from socioeconomic-induced changes in demand patterns within the broader framework of energy efficiency measures.