Implementing Energy Efficiency Research Articles

Evaluating energy efficiency strategies in a tiny house located in a cold dry climate

The ‘Tiny House’ (TH) is a classification of residential construction, which typically have less than 400 ft2 (37 m2) of floor area. The smaller size of components and overall construction pose unique challenges to designers and builders of such houses. Therefore, this study leverages existing literature to identify optimal building practices, materials, and processes for the design, construction and operation of THs that reduce energy consumption while at the same time ensure thermal comfort of occupants. The study then evaluates selected strategies for the envelope, ventilation, as well as for passive heating and cooling, that can potentially contribute to the reduction in energy consumption and maintain appropriate thermal comfort in a TH. The intent of this study is to determine the impact of the unique configuration of the TH on resulting energy performance and thermal comfort. A calibrated whole-building simulation model of a TH in Bozeman, Montana is used to evaluate the measures taken to improve energy efficiency and thermal comfort in the house for the specific climate conditions. Results indicate to overheating as a major concern when implementing energy efficiency strategies in the TH. Recommendations for such strategies include: the use of construction technologies that provide improved insulation and airtightness; implementation of passive ventilation strategies, optimizing the use and position of windows; and implementation of hybrid passive cooling systems, all of which have to address the issue of overheating with adequate ventilation in the TH.

Energy Complexity of Convolutional Neural Networks.

The energy efficiency of hardware implementations of convolutional neural networks (CNNs) is critical to their widespread deployment in low-power mobile devices. Recently, a number of methods have been proposed for providing energy-optimal mappings of CNNs onto diverse hardware accelerators. Their estimated energy consumption is related to specific implementation details and hardware parameters, which does not allow for machine-independent exploration of CNN energy measures. In this letter, we introduce a simplified theoretical energy complexity model for CNNs, based on only a two-level memory hierarchy that captures asymptotically all important sources of energy consumption for different CNN hardware implementations. In this model, we derive a simple energy lower bound and calculate the energy complexity of evaluating a CNN layer for two common data flows, providing corresponding upper bounds. According to statistical tests, the theoretical energy upper and lower bounds we present fit asymptotically very well with the real energy consumption of CNN implementations on the Simba and Eyeriss hardware platforms, estimated by the Timeloop/Accelergy program, which validates the proposed energy complexity model for CNNs.

Implementation of Energy Efficiency and Visual Comfort in Malang’s Foreign Exchange Office Lighting

The Victory International Futures office is running on foreign exchange. It is located in East Java, Indonesia, in Malang. The furniture in this office has an HPL finish, which is typically dark and has a brown cotton color. The light source’s reflections will be lessened as a result. The southeast-facing Victory International Futures building has good lighting. However, the lighting may be improved due to the limited artificial lighting offered in some areas and the absence of windows, which prevents daytime sunshine from reaching its full potential. Using windows where none are present results in an improper exchange of air coming in and going out. This study intends to ascertain the effects on workers caused by the influence of office air circulation and light intensity. This study’s technique, a case study, requires that we visit the office right away. Data gathering methods included surveys, interviews, and using the Light Metre program to gauge light intensity. According to the test results, the typical room has more light than it needs at night. Through smart lighting, this research proposes designs to enhance the quality of conference room interiors, particularly lighting design. A standard light distribution of 73,9 lux is achieved by simulating one type of lamp already present in the space using the Light Metre application.

Barriers to the implementation of energy-efficiency practices in the U.S. retail sector: facilities managers’ perceptions compared by facility size and business revenue

PurposeThe retail sector has the largest energy consumption among commercial buildings in the U.S. Although previous studies explored benefits, barriers and solutions for implementing sustainability in various building sectors, research focused on retail facilities has been very scarce. This study aims to explore U.S. facilities managers’ perceptions of barriers that prevented the implementation of energy-efficiency practices in the retail sector. Their perceptions were compared by facility size and facilities management company’s business revenue.Design/methodology/approachAn online survey was distributed to the members of the International Facility Management Association and the author's LinkedIn network. The survey responses were analyzed using descriptive statistical analysis and ANOVA.FindingsManagers from large facilities, as opposed to those from small ones, significantly more agreed that the unavailability of building automation systems, a lack of professional writing skills and a lack of awareness of life cycle cost (LCC) were the barriers. Business revenue did not cause significantly different perceptions of the barriers except for a lack of awareness of LCC and a lack of support from upper management.Originality/valueThis study fills the research gap on energy efficiency in the retail sector by revealing U.S. facilities managers’ perceptions of the barriers to the implementation of energy-efficiency practices in retail stores. This novel study compares perceptions of the facilities managers by facility size and business revenue; this comparison has not been performed before. The study also identified several new barriers to the implementation of energy efficiency in the retail sector.

REGIONAL ENERGY MANAGEMENT SYSTEMS BASED ON ESCOs: FEATURES OF CREATION AND APPLICATION

Excessive consumption and inefficient use of fuel and energy resources, unsatisfactorily high energy intensity of production of goods and services and their associated low competitiveness in domestic and foreign markets, in the context of a shortage of own energy resources and constant growth of prices for them, puts the problem of improving energy efficiency and energy saving of the regional and, as a result, national economy in the category of primary importance and relevance. The solution to this problem is achieved by creating a regional energy management system in regions (districts) on the platform of regional energy service companies, which allows, through a unified energy policy for the development and implementation of energy efficiency and energy saving projects in the region and the introduction of renewable energy sources, to more effectively address the problems of attracting foreign investment in the recovery and innovative development of the regional economy.

A Method and Metrics to Assess the Energy Efficiency of Smart Working

The paper discusses the energy efficiency of smart working (SW) as a solution to traditional work-approach issues, with a focus on evaluating benefits for both employers and employees. Remote working, while offering environmental advantages such as reduced commuting and office space use, poses challenges in assessing its true impact. The study presents results from a dynamic analysis on a real residential building, typical of an architectural style diffused in northern Italy, revealing a 15% average increase in energy consumption when all work tasks are performed from home. To address concerns about the environmental impact of SW, the research proposes a method and metrics for evaluation. Four novel indices (SWEET, SEE, SSEE, and 4E) are introduced, providing a structured approach to assess the energy efficiency of SW initiatives. The paper outlines a methodology for data gathering and metric application, aiming to acquire quantitative insights and mitigate disparities arising from a transfer of burdens to employees. This contribution not only signifies a ground-breaking methodology but also addresses an unresolved research question concerning the evaluation of the actual energy efficiency of smart working implementations for both employers and employees. The results underscore the importance of understanding the nuances of SW’s impact on household energy usage and its broader implications for sustainability goals.

An Embedded Machine Learning Fault Detection System for Electric Fan Drive

Industrial fans are critical components in industrial production, where unexpected damage of important fans can cause serious disruptions and economic costs. One trending market segment in this area is where companies are trying to add value to their products to detect faults and prevent breakdowns, hence saving repair costs before the main product is damaged. This research developed a methodology for early fault detection in a fan system utilizing machine learning techniques to monitor the operational states of the fan. The proposed system monitors the vibration of the fan using an accelerometer and utilizes a machine learning model to assess anomalies. Several of the most widely used algorithms for fault detection were evaluated and their results benchmarked for the vibration monitoring data. It was found that a simple Convolutional Neural Network (CNN) model demonstrated notable accuracy without the need for feature extraction, unlike conventional machine learning (ML)-based models. Additionally, the CNN model achieved optimal accuracy within 30 epochs, demonstrating its efficiency. Evaluating the CNN model performance on a validation dataset, the hyperparameters were updated until the optimal result was achieved. The trained model was then deployed on an embedded system to make real-time predictions. The deployed model demonstrated accuracy rates of 99.8%, 99.9% and 100.0% for Fan-Fault state, Fan-Off state, and Fan-On state, respectively, on the validation data set. Real-time testing further confirmed high accuracy scores ranging from 90% to 100% across all operational states. Challenges addressed in this research include algorithm selection, real-time deployment onto an embedded system, hyperparameter tuning, sensor integration, energy efficiency implementation and practical application considerations. The presented methodology showcases a promising approach for efficient and accurate fan fault detection with implications for broader applications in industrial and smart sensing applications.

Evaluation of energy efficiency and renewable energy utilization in the road transportation sector of indonesia to reduce emissions in accordance with national contribution target

The transportation sector in Indonesia is one of the largest contributors to GHG emissions after the energy sector, which holds 28.4% of total GHG. Based on data from 2019, the road transportation sector contributes for 70-80% of the emissions. The implementation of energy efficiency and the utilisation of renewable energy (RE) in the road transportation sector play a crucial role in reducing GHG emissions and achieving national climate change contribution targets. These policies are outlined in the Minister of Transportation of the Republic of Indonesia Decree (KEPMENHUB RI) No. KM 8 of 2023, which sets out concrete steps to be taken. Despite this policy, decarbonizing the road transport sector requires restructuring the way people move. The transformation should engage in four areas such as activity reduction, modal choice and supporting infrastructure, energy intensity, and fuel carbon intensity, which only can be successful by changing the human behaviour on consumption and intensify innovation and technology development. Carbon pricing can be the alternative to power this shifting. Theoretically, carbon pricing provides economic incentives for transport manufacturers and consumers to adopt mitigation measures. This research aims to analyse the effectiveness of KEPMENHUB RI No. KM 8 of 2023 in reducing road transport emission and identifying the potential complimentary policy such as carbon pricing scheme in transportation. The research methodology includes literature review, analysis of policy implementation data, and GHG emissions assessment model of each regulatory and policy implementation scenario. During the analysis process, the mitigation action policies in KEPMENHUB RI No. KM 8 of 2023 are categorised according to the Avoid, Shift, Improve (ASI) framework. Policy, emission, environment, and market factors are applied to the GHG emissions assessment model. Final results highlight the extent of realisation, level of effectiveness, and the ease of implementation for each scenario.

Does innovation facilitate meeting the CO2 emission reduction targets of China: A non-linear approach

China has been implementing energy efficiency and CO2 emission reduction schemes at the provincial level that have been embedded in the National Five Year Plans of the country. We set out to investigate the relationship between R&D expenditures and CO2 emissions in China at the province level in the context of the planned emissions reduction targets. We explore the possibility of the existence of a non-linear relationship between R&D expenditures and CO2 emissions with a non-parametric methodology, a fixed effect panel data quantile (FEQR) regression estimator applied to a panel of 30 provinces. We stratify the sample according to the five emissions reduction target tiers of the 12th Five-Year National Plan of China and we investigate the role of R&D expenditures in emissions reduction within each of the tiers. We find an inverse U relationship with different turning points for the three middle tiers and a U-shaped relationship for the tier under the most stringent environmental regulation. We find no effect in the tier with the least stringent emissions reduction targets. A further investigation shows that the above results are attributed to sectors with relatively low energy intensity and not to the sectors of heavy industry. The results allow us to draw broad conclusions about the effectiveness of investment in new technologies as a means of meeting the CO2 targets in China.

Renewable energy integration for lower emission planning and achieving regional energy independence: study in Yogyakarta

We propose and try to find a model renewable energy integration for lower emission and achieving regional energy independence. The research uses three stages. First, building an energy system model, to analyze the impact of implementing energy efficiency programs. The second stage is developing an integrated model from the energy demand and supply. Finally, arranging a mitigation scenarios to develop a low-carbon energy and achieving energy independence. Long-range Energy Alternative Planning (LEAP) software was utilized to simulate two scenarios are used to analyze energy demand and supply, namely Baseline scenarios and Mitigation scenarios. Mitigation Scenario is a combination of Energy Efficiency (EE) scenario, Fuel Substitution (FS), mode change scenario (MC), and efficient vehicle (EV) scenario and is used to analyze their impact on energy demand and GHG emissions. The results showed that mitigation scenarios can reduce energy demand by 22.86% when compared to the baseline scenario. In 2050, energy demand based on the mitigation scenario is 147.15 x 103 TJ, higher than based on the baseline scenario, which energy demand is 113.52 x 103 TJ. The cumulative of GHG emission reaches 1,369.37 x 103 Tons of CO2 Equivalent, based on the baseline scenario, can be reduced 13.54% by mitigation scenario. These results are robust. Overall, this research finding provides some possible steps to develop Yogyakarta energy independence and other regions.

Energy efficiency investment in Swiss industry: Analysis of target agreements

Slowing down the energy efficiency (EE) improvement rate over the past half a decade and relatively low EE-related investments highlight a need for a more targeted approach to encourage EE improvement investment in the Swiss manufacturing sector. The current work is based on an analysis of already implemented energy efficiency measures (EEMs) via target agreements. Four segments of the Swiss manufacturing sector are identified using the K-means clustering based on the energy consumption profiles of the establishments. The EEMs are categorized through text analysis with the help of a machine learning algorithm. The EEMs are ranked using two indicators that can inform different stakeholders: i) EEMs with Internal rates of return well above the reported risk-adjusted hurdle rates are identified for each segment to help industrial decision-makers take ad-hoc, profit-based investment decisions on EE improvement. ii) Levelized costs of saved energy are presented as value ranges for the EEMs that have the largest impact on annual total final energy (TFE) reduction for each segment to help policymakers design a more targeted approach for the promotion of high-impact EEMs. EE system improvements such as insulation of steam systems and process optimization, as well as EE equipment improvements like process equipment insulation, installation of variable frequency drives (VFDs), smart controls, and IE3 motors, were found to be the most profitable options across all clusters. For the electricity cluster, CHP was found to be the most impactful way of EE improvement (high energy savings) but not cost-effective. For fuel-consuming clusters, waste heat recovery measures were found to be the most cost-effective and among the most impactful options for EE improvement, along with fuel substitution measures with varying degrees of cost-effectiveness.

ENERGY EFFICIENCY MANAGEMENT TOWARDS SUSTAINABLE DEVELOPMENT: THE NEED FOR AN ENERGY EFFICIENCY LEGAL FRAMEWORK

Issues on the ever-growing demand for energy, limited fossil fuels, difficulties in obtaining viable, sustainable energy for sustainable development, and adverse impacts on global climate change have driven the international community towards the use of cleaner and more sustainable energy sources. In addition to the sustained availability and utilisation of renewable energy sources, many countries have developed and implemented energy policies to address the above issues as well as to conserve energy in line with Goal 7 of the Sustainable Development Goals (SDGs). This doctrinal analysis is performed to identify the extent to which energy efficiency and conservation imperatives in Malaysia have been implemented and explore the need to enact a legal framework for energy efficiency in this country. The aim of this paper is to detail the prospects of energy efficiency in Malaysia and discuss efforts made to enable energy efficiency practices with a view toward the sustainability of energy supply for future use. The outcomes of this study demonstrate that the implementation of energy efficiency in the country is consistent with one of the core agenda items for the economies to progress as recommended by the United Nations General Assembly through SDG 7. Nevertheless, without specific legislation for the implementation of energy efficiency, efforts to strengthen the management and implementation of energy efficiency in Malaysia will not be possible.

Partisanship and energy efficiency program participation in the USA

Utilities and municipal governments often implement energy efficiency programs to encourage households to improve the energy efficiency of their residences through measures such as weatherizing their homes, installing insulation, replacing inefficient appliances, setting up solar panels, among others. However, these programs often meet with variable success, with some having relatively few participants despite the promise of cost-savings for homeowners. Even as municipal-scale, home energy efficiency programs have grown in popularity, political polarization has increased in the USA. We suggest that political partisanship is an under-studied but potentially important predictor of program participation and use two datasets from Fort Collins, CO to evaluate the relationship between partisanship and energy efficiency program participation Our results imply that partisanship has a modest effect on program participation—this effect is also not highly robust. These results are encouraging in that they imply that partisanship does not greatly affect household decision-making to engage in energy efficiency programs, but at a grid or regional scale partisanship may act as a small barrier to energy efficiency.

Промышленный энергоменеджмент: оценка системы принятия решений

Increased concern about the state of the environment and, accordingly, energy problems in the 70s and 80s. last century significantly influenced the development of new policies and strategic decisions to reduce energy losses and its rational and efficient use. Reducing energy consumption through an annual reduction in energy intensity is an indicator of the implementation of energy efficiency policies. The industrial sector, being one of the main consumers of energy, pays great attention to improving its energy performance. Therefore, increasing attention is being paid to understanding the energy status, as well as the barriers and potential opportunities for implementing energy efficiency and energy conservation programs in the enterprise. One of the main roles in increasing the level of energy efficiency and energy saving of industrial enterprises is played by the energy management system, which implements relevant programs and practices. However, due to the lack of information or knowledge, as well as the high degree of uncertainty and risk in energy markets, the problem of decision making is still relevant at the very first stage of implementing an energy management program at an enterprise, so the importance of determining the parameters that should be included in the decision-making process increases. solutions, and developing an energy management strategy in accordance with the complexity and characteristics of each company. Achieving the energy transition will depend not only on the introduction of new and innovative energy-related technologies, but also on significant and in some cases radical changes in the decisions and behavior patterns of decision makers. Given that budget allocation for any investment must have not only financial but also strategic value for the company, this paper aims to identify the parameters that influence decision-making in the energy sector in industry, taking into account the critical role of energy management systems, energy efficiency and energy saving.

Beyond policy impacts: Internal strategic capabilities as determinants of industrial energy efficiency implementation

Many countries have implemented policies to encourage higher Energy Efficiency (EE) implementation, but they seem insufficient and significant EE opportunities are unrealized. It is thus important to understand which internal strategic drivers can help further improve EE implementation by a firm. We develop a theoretical framework and empirically investigating how corporate strategy influences firms' EE implementation on top of EE policies. Drawing from the literature on Natural-resource-based view and Proactive Environmental Strategy, we hypothesize the impacts of several strategic capabilities, including shared vision, top management support and stakeholder integration on a firm's EE implementation. We test our hypotheses using combined secondary data of 572 publicly traded firms from the CDP report, COMPUSTAT Fundamentals North America and Global, and the World Economic Forum. Our study enriches the literature on firms' perceived EE drivers by dissecting the nuanced impacts of different strategic capabilities and exploring new dimensions. Our findings also suggest important policy insights in encouraging higher industrial EE implementation. It remains important to strengthen EE policies and regulation. However, to make further leaps in EE implementation, policy efforts could be spent in information campaigns helping redirect interpretation of EE improvements toward opportunities and away from risks/threats.

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.

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.

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.

GEECO: Green Data Centers for Energy Optimization and Carbon Footprint Reduction

Cloud computing has revolutionized data storage, processing, and access in modern data center operations. Conventional data centers use enormous amounts of energy for server operation, power supply, and cooling. The processors produce heat while processing the data and therefore increase the center’s carbon footprint, and the rising energy usage and carbon emissions caused by data centers pose serious environmental challenges. Under these circumstances, energy-efficient green data centers are being used as a phenomenal source of sustainable modernization. This study proposes the implementation of the Green Energy Efficiency and Carbon Optimization (GEECO) model for enhancing energy usage. Within the data center, the GEECO model dynamically adjusts workload distribution and task assignment to balance performance and manage service-level reconciliation. The ability to identify possibilities for energy optimization and carbon emission reduction is possible through real-time monitoring of energy usage and workload demand. The results revealed a considerable increase in energy efficiency, with significant decreases in energy usage and related costs. The GEECO model provides a significant improvement in energy consumption and carbon emission reduction for the different introduced scenarios. This model’s introduction to practical application would be made possible by these improvements in the quantitative results. The approach of this study also has a positive impact on the environment by reducing carbon emissions. The resilience and practicality of the solution are also analyzed, highlighting the probability of widespread adoption and its associated improvements in the advancement of sustainable cloud computing.

Characterizing sector-wide thermal energy profiles for industrial sectors

Process heat electrification, energy efficiency improvement, and renewable technology integration are the most effective ways of achieving industrial decarbonization. However, precise knowledge of thermal energy demands and excess heat sources along with the corresponding temperature levels is imperative for the most optimum integration of aforementioned process. The current study presents a novel methodology for constructing sector-wide composite curves to accurately characterize the thermal energy demands and the available excess heat sources for the Swiss industry subsectors. The sector-wide energy demand profiles derived from this methodology offer valuable insights into the quantity of heat demand and the quality of the available excess heat. Thus, enabling the implementation of energy efficiency and decarbonization technologies such as heat pumps, and renewable heating and cooling technologies. The sector-wide energy demand profiles are built using Pinch Analyses and top-down statistical approach, thus ensuring complete sectoral coverage. The application of this novel approach is demonstrated using Swiss meat and chocolate industry subsectors as case studies. Based on the sector-wide energy demand profiles developed for meat and chocolate production, heat pump integration and solar thermal integration can potentially reduce about 80% of process heat demand, for both sectors.