Residential Energy Efficiency Research Articles

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.

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.

Building a Sustainable Future: How Eco-Friendly Homes Are Driving Local Economic Development in Lisbon Metropolitan Area

This article explored the impact of eco-friendly houses on economic development in the Lisbon metropolitan area. The study analyzed data from 18 municipalities between 2014 and 2020 using regression analysis with ordinary least squares (OLS) and fixed effects. The results indicate that national policies promoting residential energy efficiency positively impact economic development. Policies like subsidies, loans, and tax relief encourage homeowners to invest in energy-efficient technologies, boosting household disposable income and contributing to economic growth. The study found that an increase in the number of new constructions positively impacts economic development, leading to job creation and increased demand for construction materials and services. Additionally, growing eco-friendly houses can reduce energy consumption, lowering energy costs for homeowners and businesses and ultimately stimulating economic growth. In contrast, increasing the number of non-eco-friendly houses can have negative economic impacts. A robustness check using the method of moments quantile regression (MM-QR) confirmed the results from OLS with fixed effects, providing additional evidence supporting the robustness of the results.

Effects of redesigning the communication of low-income residential energy efficiency programs in the U.S.

Researchers have speculated that limited participation in low-income energy efficiency programs is due, in part, to poor communication of program information. In response, we modified Bamberg’s stage-based model of behavior change (SSBC) to understand decision-making amongst low-income residents regarding residential energy efficiency programs. We then used the model to develop a framework for creating more informative communications. As a test of the framework, we redesigned web pages describing low-income energy efficiency programs in accordance with the framework and examined the effect of the change by randomly assigning low-income survey participants (n=348) to an original or redesigned version of the web page for three electric distribution companies. We found that redesigning these web pages directly, or indirectly, via mediation from likeability of a web page, improved low-income respondents’ assessment of program eligibility; increased their ratings of the importance and value of program outcomes; and increased their intentions to apply for and complete the program. The results signify the legitimacy of the framework as a method for reducing barriers to participation in energy efficiency programs, thereby having practical implications for the organizations and policymakers seeking to increase involvement in these programs. We discuss predictors of application intentions and implications for program communications.

Advanced Active and Passive Methods in Residential Energy Efficiency

Energy efficiency in buildings is very important since it contributes significantly to fossil fuel consumption and consequently climate change. Several approaches have been taken by researchers and the industry to address the issue. These approaches are classified as either passive or active approaches. The purpose of this review article is to summarize a number of the technologies that have been investigated and/or developed. In this technical review paper, the more commonly used active and passive building energy conservation techniques are described and discussed. The pros and cons of both the active and passive energy techniques are described with appropriate reference citations provided. This review article provides a description to give an understanding of building conservation approaches. In the active classification, several methods have been reviewed that include earth-to-air heat exchangers, ground-source and hybrid heat pumps, and the use of new refrigerants, among other methods. In the passive classification, methods such as vegetated roofs, solar chimneys, natural ventilation, and more are discussed. Often, in a building, multiple passive and active methods can be employed simultaneously.

Residential and Industrial Energy Efficiency Improvements: A Dynamic General Equilibrium Analysis of the Rebound Effect

The aim of this paper is to investigate bi-directional spillovers into residential and industrial sectors induced by energy efficiency improvement (EEI) in both the short- and long-term, and the impact of nesting structure as well as the size of elasticities of substitution of production and utility functions on the magnitude and the transitional dynamic of rebound effect. Developing a dynamic general equilibrium model, we demonstrate that residential EEIs spillover into the industrial sector through the labor supply channel and industrial EEIs spillover into the residential sector through the conventional income channel. Numerical simulations calibrated on the U.S. suggest that not taking into account these spillover effects could lead to misestimating the rebound effect notably of residential sector EEIs. We also demonstrate how the size and the duration of the rebound effect depend on the elasticities of substitution’s values. Numerical simulations suggest that alternative sets of value for the elasticities of substitution may give different sizable patterns of rebound effects in both the short- and long-term. In policy terms, our results support the idea that energy efficiency policies should be implemented simultaneously with rebound effect offsetting policies by considering short- and long-term economy feedbacks. As a consequence, they require considering debates about what type of policy pathways are more effective in mitigating the rebound effect.

Enabling and centering equity and justice in clean energy transition research

Enabling and centering equity and justice in clean energy transition research

How to Support Residential Energy Conservation Cost-Effectively? An analysis of Public Financial Schemes in France

We compare the performance of four types of support schemes aimed at improving residential energy efficiency in France: the income tax credit, a grant scheme, the reduction of the value-added tax, and the White Certificates. We use the TREMI dataset which covers close to 14,000 households that conducted conservation works. To address self-selection bias, we use a double-robust inverse probability weighting estimator. We assess the effect of the adoption of each scheme on the funding acquired, the private and total investment, and the reduction of the household energy expenses. For each scheme, we use the estimates to deduct its cost-effectiveness, the involved redistribution, and its ability to trigger additional investment in energy conservation works. We find funding from the schemes to reduce energy expenses most cost-effectively via the White Certificates. Redistribution is neutral for each of the four schemes: higher and lower income households equally benefit from them.

Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets: A Review

An increase in consumption and inefficiency, fluctuating trends in demand and supply, and a lack of critical analytics for successful management are just some of the problems that the energy business throughout the world is currently facing. This study set out to assess the potential contributions that AI and ML technologies could make to the expansion of energy production in developing countries, where these issues are more pronounced because of the prevalence of numerous unauthorized connections to the electricity grid, where a large amount of energy is not being measured or paid for. This study primarily aims to address issues that arise due to frequent power outages and widespread lack of access to energy in a wide range of developing countries. Findings suggest that AI and ML have the potential to make major contributions to the fields of predictive turbine maintenance, energy consumption optimization, grid management, energy price prediction, and residential building energy demand and efficiency assessment. A discussion of what has to be done so that developing nations may reap the benefits of artificial intelligence and machine learning in the energy sector concluded the paper.

Demand Side Limitations of Financing Household Energy Efficiency Investments : A Regional approach

The building sector represents 40% of the total energy use in the European Union. Without exploiting the energy efficiency potential of the sector, neither the Paris Agreement nor the related commitments of the European Union can be achieved. This paper takes a comparative regional approach, differentiating between Northern, Western and Central-Eastern Europe and aims to study the drivers for and barriers to residential energy efficiency investments based on the various demand side indicators related to the housing stock and financial capacity of households. This study finds that financing challenges are more striking in Central-Eastern Europe where households are more cost sensitive and risk averse. Although they are strongly motivated by higher energy bills relative to their income, they are often prevented from making the right investment decision by a number of market and behavioral failures. Public policy, meant to facilitate commercial lending activity on this market, should consider aiming at reducing the real or perceived costs of borrowing via grants, interest rate subsidies and portfolio guarantees. It could encourage households with similar risk profiles to form loan borrowing communities to spread, and thus mitigate, the risk of financing. The innovative public policy suggestions, addressing the demand side, presented in this paper and the topic in general require more research.

A multidimensional energy poverty measurement in China - Based on the entropy method

Although energy poverty has obtained wide focus in recent years, there is no consensus on the measurement of energy poverty. Given that China is experiencing socio-economic transformation as well as energy transition, this paper employed a total of 28 indicators, which representing three categories (energy service availability, energy consumption and generation cleanliness, and residential energy affordability and efficiency) that capture China's features as well as consider policy factors, to develop a new multi-dimensional energy poverty index by using entropy weight method. The result reveals that the energy poverty level has been improved nationwide for the period 2014–2019; three sub-indices also show downward trends while household energy affordability and efficiency suggests the fastest improvement; however, significant differences exist in energy poverty not only between urban and rural areas but also across provinces in China. Policy recommendations are as follows: continue to promote rural power grid renovation and upgrading and the construction of natural gas pipelines, increase clean energy support; encourage diversified development of rural energy supply methods, and provide more innovative and diversified green energy and financial products.