Residential Energy Efficiency Research Articles

Forecasting residential real estate prices and energy usage in Flanders

Purpose Currently, residential forecasting literature is limited with few to no forecasting papers that consider residential prices and energy consumption for the selling and rental market within a single study. The purpose of this paper is to study the prices and energy usage for both markets by combining an explanatory model with the predictions of a predictive model, which will lead to a better understanding of the forecasting results. Design/methodology/approach This study presents an econometric comparison between 508,612 selling properties and 174,007 rental properties transacted in Flanders between January 2019 and December 2023. The authors study their prices and energy usage by combining the causal results of a hedonic price model (HPM) with the predictions of an autoregressive integrated moving average model (ARIMA). Findings The authors find that both markets are extremely sensitive to changes in related factors, whereby selling prices are primarily influenced by macroeconomic factors, but rental prices and especially energy usage are also sensitive to market standards. Policies that target energy efficiency can therefore steer its future trend. In addition, in line with previous literature, the HPM shows an increasingly strong relationship between residential prices and energy efficiency over the years, indicating that these steering energy policies also indirectly monitor residential prices. Originality/value This study underscores the importance of integrating the results of an explanatory model with a predictive model. It demonstrates that combining them provides a more comprehensive understanding which is helpful for policymakers, real estate professionals and investors during their decision-making process.

Sustainable energy transition, knowledge co-creation and trade: the case of EU residential energy efficiency

ABSTRACT The European Union (EU) sustainable energy transition requires radical changes of economic, technological, societal and political systems. This paper empirically investigates the relationships among environmental policy, innovation and technological capabilities, and economic performances, in terms of country export competitiveness, focusing on residential energy efficiency goods, a core pillar of the EU energy and climate strategy. We combine network analysis with a gravity model to investigate the evolution of the EU bilateral trade and co-patenting activities from 1990 to 2015, based on a panel of 19 European countries. Our results show that the technological skills of the exporting country, related to the production of the households’ energy-consuming appliances, strongly enhance its export performances, and this effect is stronger when countries are EU members. Cooperation in knowledge creation and institutional proximities between trade partners are further key determinants of export performances of analysed countries.

Synergistic Neural Network and Velocity Pausing Particle Swarm Optimization for Enhanced Residential Building Energy Efficiency: A Case Study in Kuwait

The global energy demand of buildings is on the rise, driven by factors such as rapid population growth, increasing comfort, technological advances, and ongoing developments in building construction. This escalating energy consumption in buildings is a major contributor to the energy crisis and climate change. Accurate prediction of building energy consumption is essential for gaining insight into energy utilization, reducing waste, and enhancing comfort conditions. This study aimed to introduce a reliable technique for predicting and optimizing the energy consumption of residential buildings, with a focus on a case study in Kuwait. A robust Artificial Neural Network (ANN) was developed, meticulously trained, and rigorously tested to provide accurate energy consumption predictions. Subsequently, an innovative variant of the Velocity Pausing Particle Swarm Optimization (VPPSO) algorithm was employed to identify optimal energy consumption solutions. This novel optimization technique can achieve significant reductions in building energy consumption, with potential savings of up to 43%. Additionally, a sensitivity analysis was performed using the Garson method to assess the impact of input parameters on energy utilization. The results reveal that the insulation and cooling setpoint exert the greatest influence on the objective function, followed by the outdoor airflow. The proposed model, which combines the power of ANN with VPPSO, can be applied to similar buildings, offering precise predictions and optimizing energy consumption. This approach holds promise in addressing the pressing challenges of energy efficiency in building environments.

Achieving economy‐wide gains from residential energy efficiency improvements: The importance of timing and funding approach in driving the transition

AbstractResidential energy efficiency improvements are generally considered integral to achieving climate change targets. Alongside the primary benefits of reducing energy use and consumer bills, there is increasing policy interest in the potential for energy efficiency programmes to deliver economy‐wide gains, measured by gross domestic product, employment, household real incomes and spending power etc. Our previous research shows that such sustained gains are likely over time. Here, we consider how transitory outcomes are likely to be heavily influenced by the timing of actions and who pays, how and when. Insight in this regard is crucial for policy makers considering the mix and timing of measures to reach net zero outcomes that are economically as well as technically feasible. We consider alternative funding, distributions and timeframes for residential retrofitting costs and projects using a UK economy‐wide scenario simulation model. The key insight is that while government support for the provision of low‐cost finance options is strategically important in alleviating budget constraints and mitigating potential short‐term negative impacts on household spending, producer responses to the wind down of retrofitting spending can disrupt the adjustment of the economy. Here we identify pros and cons of different trajectories of action towards high‐level energy efficiency policy targets.

Split Incentives in Emerging Countries

In this paper, we provide empirical evidence of the energy-efficiency gap between homeowners and renters and quantify the magnitude of the split incentives problem in an emerging economy by studying Mexican households. Using micro-level data from the first National Survey on Energy Consumption in Private Homes (ENCEVI-2018) and a regression framework, we show that underinvestment problems occur in multiple categories of residential energy efficiency. Concretely, our results show that renters have significantly less insulation and energy-efficient equipment, that they tend to use some of their equipment more frequently, and that they pay higher utility bills than homeowners. In addition, renters are less aware of government programs that can reduce their energy expenditure and are also less likely to take advantage of them. Finally, a substantial reduction in carbon emissions could be achieved if renters were equally energy efficient as homeowners. JEL classification: D12; Q15; Q40; Q53

Linking energy efficiency and waste recovery to improve housing insulation and tackle energy poverty in south-central Chile

Given the environmental impact of the construction industry, sustainable solutions are increasingly needed to minimize the life cycle impacts of buildings by means of reusing renewable industrial waste. Abundant forest and farm waste exist in center-south Chile, with great potential to be used as thermal insulation and, thus, improve the livability of houses located in an area facing air pollution and energy poverty issues. This paper discusses two types of insulation produced and tested jointly with Germany-based Fraunhofer Institute for Wood Research (WKI)– using Chilean agroforestry waste: First, a flakeboard made of wood veneer chips; and second, a flexible substrate and a pressure resistant board made of wheat and oat straw. The main goal of this research is to assess, by means of digital modeling and experimental tests, the thermal characteristics and the resistance to humidity of these new insulation materials and their performance within construction solutions usually used in Chile. The benchmarks used were the most demanding national residential energy efficiency standard implemented to address air pollution and energy poverty, and the most demanding international standard, both for walls and roofing. The results of both insulation materials was promising, as they met the thermal performance and resistance to humidity requirements and show technical feasibility for the insulation market; in addition, they share the potential to be used in different types of buildings and construction solutions both in Chile and abroad, promoting a sustainable approach starting from the raw materials and the manufacturing process, through the buildings’ operations.

Residential renovations: Understanding cost-disruption trade-offs

Disruption and mess associated with energy efficiency retrofits is one aspect that is rarely considered in studies investigating households’ preferences for energy renovations. Using a choice experiment, we estimate a price for residential retrofit disruption, finding it represents a substantial proportion of associated energy cost savings among some households. There is considerable variance in willingness to pay for both energy cost savings and disruption avoidance, consistent with experience of many households investing in energy retrofits but also of government retrofit schemes falling far short of policy targets. Just 1-in-4 households are actively receptive to retrofit policy supports, and disruption posing a significant barrier to undertaking energy retrofits, means both contribute to the slow progress against public policy targets to improve residential energy efficiency.

Deep learning–based urban energy forecasting model for residential building energy efficiency

Sustainable and inventive city design is becoming more and more dependent on the use of cutting-edge technology as smart cities develop further. Energy efficiency optimization in residential structures is an essential part of the puzzle as it helps conserve resources and keeps the planet habitable. An enhanced Deep Neural Network (DNN) model for household energy efficiency predictions is presented in this research. Our model uses a large dataset of building features, weather, occupancy patterns and energy usage histories. Data is preprocessed, features are engineered and hyperparameters are tweaked to improve DNN prediction. Scalable, easy-to-understand models are essential, as are shifting urban areas and energy landscapes. In this work, the authors have evaluated the proposed model with basic model with different optimizers. Initially, the Stochastic Gradient Descent optimizer applied that gained 91.02% Recall, 93.47% Precision, 93.28% F1-Score, 0.0153 MSE, 0.0166 RMSE and 0.0165 MAE. The proposed model gained 99.52% Recall, 98.91% Precision, 99.09% F1-Score, 0.0140 MSE, 0.0137 RMSE and 0.0139 MAE. By monitoring, analyzing and making decisions in real time, smart city systems can help planners understand energy usage trends. The optimized DNN model advances smart city development by promoting sustainability and resource optimization. Predicting residential buildings’ energy efficiency provides proactive energy savings, cost reduction and environmental impact mitigation. The suggested DNN model shows how smart cities use cutting-edge urban planning to become more sustainable, efficient and resilient.

Assessing Residential Building Energy Efficiency Using Evolutionary Dendritic Neural Regression

Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain.

CFD Simulations of Small Particle Behavior with Blower-Driven Airflows in Single-Family Residential Buildings

Inhaling airborne droplets exhaled from an infected person is the principal mode of COVID-19 transmission. When residential energy efficiency workers conduct blower door tests in occupied residences with a COVID-19-infected occupant potentially present, there is a concern that it could put the workers at risk of infection with massive flows of air being generated by the tests. To minimize this risk, computational fluid dynamics (CFD) simulations were conducted for four prototype houses to develop guidelines for workers to follow during their service visits. The CFD simulations visualized the movements and evaluated the residence time of small particles released at certain locations under a series of scenarios representing situations that are likely to be encountered during in-home energy efficiency services. Guidelines were derived from the simulated tracks of droplets to help to increase the safety of the worker(s).

Technological Trends Driving Utility Residential Energy Efficiency Program Savings

ABSTRACTIn 2023, with increasingly broad adoption of conventional utility energy efficiency programs such as rebates or customer incentives on water heaters, furnaces, and home insulation levels, new and next‐generation energy efficiency programs will likely involve increasingly connected devices and technological interventions. This article identifies four technological trends driving utility residential energy efficiency programs for the future. These trends include: (1) home energy reports (or comparative usage efficiency reports); (2) connected reinforced programs including utility pledge programs and demand response programs; (3) smart thermostats; and (4) smart lighting. This article indicates that ultimately, greater convergence around technology is likely to persist given utility investment in Automated Meter Integration (AMI) systems and broader customer‐driven technological adoption. Ultimately, this article advocates for continued broad‐based adoption of these programs to save energy and mitigate the impacts of climate change.

Bridging the Energy Efficiency Gap for Low-Income Households in Charlottesville

Residential energy usage in Charlottesville, Virginia, accounts for nearly 30% of the city’s greenhouse gas emissions. Without significant changes, Charlottesville is unlikely to meet its goal of reducing emissions by 45% by 2030. Improving residential energy efficiency is key to lowering emissions and reducing energy costs for residents. However, the high upfront cost of these upgrades is unaffordable for over a third of Charlottesville households. As electricity prices rise due to Virginia’s upcoming carbon dioxide cap-and-trade program, low-income households that can’t afford efficiency upgrades will face higher energy bills and greater financial strain. This study explores the feasibility of a residential efficiency financing program designed to improve the affordability and accessibility of energy upgrades for low-income households in Charlottesville. Through a detailed benefit-cost analysis, various policy alternatives, including On-Bill Financing, Revolving Loan Fund, and Loan Loss Reserve, were evaluated for their effectiveness, equity, and implementation feasibility. The recommended option, On-Bill Financing, would allow Charlottesville Gas to cover the upfront costs of energy efficiency upgrades. Customers would repay the investment over time through their gas bills, thereby enjoying immediate reductions in energy consumption and costs. This policy would fairly enhance residential energy efficiency and provide critical financial assistance to households that otherwise couldn’t afford these upgrades.

Optimization of Home Energy Management Systems in Smart Cities Using Bacterial Foraging Algorithm and Deep Reinforcement Learning for Enhanced Renewable Energy Integration

This paper presents a pioneering exploration into the optimization of Home Energy Management Systems (HEMS) through the novel application of the Bacterial Foraging Metaheuristic Optimization (BFMO) algorithm and Deep Reinforcement Learning (DRL). The study systematically addresses the pressing challenge of enhancing residential energy efficiency, focusing on dynamic appliance scheduling within HEMS. A robust methodology is established, encompassing data collection from smart homes, implementation details of the BFMO algorithm, DRL techniques, and a comprehensive evaluation framework. The unique contribution of this research lies in the effective integration of the BFMO algorithm and DRL to orchestrate energy‐conscious scheduling of home appliances within HEMS. The BFMO algorithm demonstrates its adaptability to fluctuating energy costs and consumption patterns by simulating the foraging behaviour of bacteria. At the same time, DRL enhances the system’s ability to learn and optimize scheduling decisions over time, showcasing their combined efficacy in real‐world scenarios. The algorithms’ iterative application of chemotaxis, reproduction, elimination‐dispersal, swarming, and learning consistently yields optimized appliance schedules. The main focus of this study resides in the evaluation metrics illustrating the tangible benefits of BFMO and DRL compared to traditional HEMS. Significant reductions in total energy consumption and cost, accompanied by improved peak demand management, exemplify the algorithms’ impact. Furthermore, the study delves into enhancing user comfort, integrating renewable energy sources, and the overall robustness of HEMS, all demonstrating the multifaceted advantages of the BFMO and DRL approaches. This research contributes methodologically by introducing and detailing these algorithms and provides a valuable dataset and evaluation metrics for future research in the domain. The findings underscore the immediate and long‐term relevance of optimizing HEMS with BFMO and DRL, catering to researchers, practitioners, and policymakers involved in advancing smart grid technologies and sustainable residential energy management. In summary, this work establishes the BFMO algorithm and DRL as pioneering and versatile tools for energy‐conscious appliance scheduling in HEMS, offering a substantial leap forward in the quest for efficient and sustainable residential energy management.

Residential Energy Efficiency

Residential Energy Efficiency

A dollar well spent: Monetizing the societal benefits of low-income weatherization programs in the United States

Low-income residential energy efficiency programs save energy and can yield numerous co-benefits or non-energy impacts (NEIs). This paper presents a two-component framework that can be used to measure and monetize household-related NEIs. Two examples of using the framework are presented, one associated with an evaluation of a low-income single family home weatherization program implemented in the Southeastern United States (U.S.) and a second associated with a broad NEI assessment of weatherized affordable multifamily buildings in the Northeastern and Midwestern regions of the U.S. Seventeen NEIs were subject to monetization, including reductions in medical interventions for being too cold or too hot in one's home and reductions in use of predatory loans. The results of the two monetization exercises indicate that the monetary value of the NEIs is equal to or exceeds the energy efficiency job costs and energy cost savings. It is also clear that the value of the NEIs differs quite substantially based on home type. Future research can focus on better estimating changes in NEI variables and expressing ranges of uncertainty in final monetization values.

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.

Development of Design Optimization for Smart Grid (DOfSG) Framework for Residential Energy Efficiency via Fuzzy Delphi Method (FDM) Approach

The smart grid revolution has benefited many sectors but the potential for design optimization among residential units has yet to be explored. Despite some researchers having negative perception of house design's association with the smart grid system, there is in fact potential for investigating design attribute optimisation aligned with the smart grid system. As electricity becomes a necessity of the 21st century society, residential dwellers are becoming more dependent on this indispensable source of energy. As such, this paper explains the development of a framework focusing on design optimization for residential units aligned to the smart grid system using the Fuzzy Delphi Method approach. It focuses on the significant smart grid components linked to the residential sector incorporating key design attributes for energy optimization purposes. The proposed framework denoted two main components of residential design optimization, depicted as indoor and outdoor parameters with its subsequent attributes further categorised into main and detailed components. Twelve design parameters were found to be substantial for the DOfSG development, intended to provide useful guide for aligning residential design towards the smart grid system in Malaysia.

Awareness and Utilization of Incentive Programs for Household Energy-Saving Renovations: Empirical Findings from Greece

The Greek policy targeting residential energy efficiency has launched the “Residential Energy Saving” financial incentives program to assist in achieving the goals related to reducing energy use and GHG emissions. Considering the research gaps of previous work, the present study examines the sociodemographic, dwelling, and geographical characteristics, and the environmental awareness and behavior that affect individuals’ (a) actual decision to perform an energy-saving renovation of their dwelling; this has been previously examined, but not in the Greek context, (b) awareness of the incentives program; this has not been addressed in previous research, and (c) utilization of the program; this has not been addressed in previous research. Accordingly, we performed a questionnaire-based survey in Greece in 2019 (n = 451). Based on the development of three binary logistic regression models, it is indicated that the decision to perform an energy-saving renovation is affected by ownership status, year of construction, income, and environmental behavior; awareness is affected by education level, environmental awareness, and residence location; use of the incentives program is affected by residence ownership and year of construction, presence of senior citizens and education level. The study’s results, indicating the determinants of energy-saving renovation decisions and awareness and utilization of financial incentives programs, can assist policymakers in planning financial incentives adapted to different characteristics, thus achieving improved awareness and utilization of such tools.

The exposure-response of air pollution and climate change to chronic respiratory diseases: Does residential energy efficiency matter?

Promoting energy efficiency is crucial for reducing energy consumption, yet its impact on human health remains discussed. This study examines the relationship between household energy efficiency, ambient air pollution, climate change, and mortality risk from chronic respiratory diseases. The study collected observational data in six major cities in Taiwan from 2008 to 2020. The energy efficiency level was determined using the input demand function derived from the stochastic frontier analysis (SFA). Subsequently, analysis was conducted employing a dynamic panel data model and a pooled mean group estimator. The study's findings indicate that enhancing household energy efficiency decreases the mortality rate associated with chronic respiratory diseases. Specifically, at the highest level of energy efficiency (99%), the relative risk reaches its lowest value of 0.639 (95% CI: 0.58–0.70). Additionally, a positive exposure-response relationship is observed between degree days and ambient air pollution (PM2.5, PM10, and SO2), associated with an increased risk of death from chronic respiratory diseases. These results underscore the importance of initiatives to enhance energy efficiency programs in households, particularly in metropolitan areas.

Residents' participation in energy performance certification for collective action on climate change: the case of EnerGuide.

Although governments have launched energy performance certification (EPC) programs to increase residential energy efficiency, residents' perception and acceptance of these programs have been little studied. This study contextualizes residents' intentions to participate in EPC programs in terms of their attributions to and attitudes toward climate change to find mediating factors and effects to help trigger collective action to reduce residential sector energy demand. This study employed a partial least squares structural equation modeling approach to empirically analyze a survey conducted on 400 residents of Edmonton, Canada, who participated in the Canadian energy efficiency rating and labeling program, EnerGuide. Using EnerGuide, a Canadian energy efficiency rating and labeling program, as an example, this study establishes a framework to explain that local residents' internal and external attributions to climate change elicit positive attitudes (need to take action), increasing their recognition of energy efficiency program benefits, which further promotes their EnerGuide program acceptance and participation intentions. This study also reveals the mediating effects between variables. Residents' attitudes toward climate change mediate the relationship between internal/external attributions and EnerGuide program acceptance, and they indirectly impact residents' program acceptance and participation intentions, with this effect moderated by energy efficiency program benefits. Residents' program acceptance also mediates the relationship between climate change attitudes/energy efficiency program benefits and the intention outcome. The study provides an example of the use of climate change discourse to motivate residents' energy efficiency program participation.