Household Energy Research Articles

Cooking Energy, Health, and Happiness of Women in Nigeria

This study utilizes novel data to investigate the impact of cooking energy sources and indoor air pollution on the happiness, life satisfaction, physical, and mental health of women in Nigeria. The existing body of literature relies on ambient air pollution data, which can be limiting in resource-constrained settings. To address this gap, we employ a direct approach, measuring Carbon Monoxide (CO) levels in participants’ blood using the Rad-57 CO-oximeter. Our analysis reveals strong positive correlations between the utilization of clean cooking energy and women’s reported happiness and life satisfaction. Additionally, the study finds that clean cooking energy usage is associated with a significant reduction in mental health problems among women. These findings highlight a substantial disparity in well-being based on access to clean cooking energy sources. Furthermore, exposure to carbon monoxide, as measured in this study, demonstrates a detrimental effect on women’s health and overall well-being. Consequently, policymakers and stakeholders should prioritize initiatives that promote household energy access and facilitate the transition to clean cooking practices, especially in rural areas where the use of polluting fuels and exposure to indoor air pollution remain prevalent concerns. JEL Classification: D12, I31, I100, Q420.

Analyzing variability and coordinated demand management for various flexible integrations of residential distributed energy resources

Deploying distributed energy resources (DERs) is crucial for decarbonizing the building sector, but challenges arise due to the energy mismatch between on-site generation and consumption. Impacts of large-scale development of distributed energy technology are seldom evaluated from a system-wide perspective. This study primarily utilizes high-resolution meter data to analyze residential energy consumption behaviors and variability of solar photovoltaics (PV), heat pump and fuel cell throughout the year. Then, rolling horizon optimization is applied to harness demand flexibility through scheduling developed integrated energy systems, considering synergies between on-site energy resources and demand side management. The techno-economic performances of the integrated systems are investigated by adopting wholesale spot prices for PV grid feed-in. Results show that flexible schedule of integrated energy systems can deliver multiple benefits, including energy saving, cost reduction and grid-support interaction, improvement in PV self-consumption through flexible coupling can exceed 40 % compared to the measured results. The effectiveness of demand-side solutions strongly depends on the density and variability of household energy consumption, micro cogeneration presents a greater potential of cost-saving and carbon reduction in winter months. Findings emphasize the importance of coordinated demand management strategy in enhancing effectiveness of energy usage and grid-friendly interactions under widespread DERs implementation.

The influence of disruptive events on energy-related household practices: Results of a longitudinal study in the Netherlands

Household energy use relates to our socio-cultural identity, our socio-economic status and the socio-cultural and material contexts in which we live. Around the world, households experienced dramatic changes in light of the COVID-19 pandemic and the drastic increase of energy prices following the Russian invasion of Ukraine. Habits and routines were disrupted, reinterpreted, reorganized and renegotiated, albeit under constraints and in most cases involuntarily. This paper analyses how the practice of ‘keeping warm’ responds to disruptive events ranging from a small-scale and short-lived experiment to much more drastic and far-reaching geopolitical events. Following an initial study challenging households to reduce temperatures inside their homes during October/November 2018, the same households were revisited for follow-up questioning two and a half years after the start of the COVID-19 pandemic in Europe and six months after the attack of Russian armed forces on Ukraine. Quantitative and qualitative data were collected before, during, and three months as well as three years after the original ‘heating challenge’. The final follow-up survey took place in August 2022 and turned the initial short-lived Living Lab-based experiment into a longitudinal study.The experimental exploration of possibilities to reduce household energy use through the disruption of routinized practices in Living Labs enables an analysis of the effects of disruptive events, including the role of values, social norms, habits and routines (Sahakian et al., 2021). But what happens, when experiments get serious and large-scale upheaval affecting many challenges and changes meanings, thus contributing to changes in social norms and practices? This research explores lasting-changes in energy-related household practices following voluntary disruptive experimentation and subsequent involuntary disruptions. Compared to the macro-systemic disruptions experienced during the COVID-19 pandemic and the more recent energy crisis, the disruptions induced through the initial ‘heating challenge’ are of a much smaller magnitude. Our findings show that reductions in energy use are possible when routinized practices are disrupted through voluntary experimentation. In addition, our results indicate that the meanings tied up with indoor comfort had changed due to the initial experiment and that participants who initially experimented in an interactive community setting had learned more about how to connect, share experiences and reflections to adapt to other, also large-scale disruptions collaboratively.

Mapping the Path to Sustainability: A Study on Household Renewable Energy Adoption Trends

This study investigates household renewable energy adoption trends in Rohini, Delhi, with a focus on women and climate change mitigation. The transition to renewable energy is crucial for combating climate change and reducing environmental impact. This research surveys 50 households to assess awareness, attitudes, and adoption barriers and drivers for renewable energy technologies owing to climate change. Findings reveal that while many are aware of government incentives and express a willingness to adopt renewable energy, actual implementation is hindered by high costs, lack of information, and technical challenges. Key drivers include environmental concerns and financial savings. The study suggests enhancing adoption through improved financial incentives, community education, and women-focused initiatives. Addressing these factors can foster broader adoption of renewable energy, contributing to effective climate change mitigation strategies.

US federal resource allocations are inconsistent with concentrations of energy poverty.

Recent data from the US Energy Information Administration reveals that nearly one in three households in the United States report experiencing energy poverty, and this number is only expected to rise. Federal assistance programs exist, but allocations across states have been nearly static since 1984, while the distribution of energy poverty is dynamic in location and time. We implement a LASSO-based machine learning approach using sociodemographic and geographical information to estimate energy burden in each US census tract for 2015 and 2020. We then compare the allocation to states from the Low Income Home Energy Assistance Program to an optimized allocation. We allocate funds to the most burdened households, providing them with enough assistance to reduce their energy expenditures so that their household energy burden is equal to a new maximum allowable energy burden. This markedly shifts funds from the northern cold-weather states to the southern warm-weather states.

Household residential energy choices in green transition: insights from a household survey in rural China.

It has been widely recognized that accelerating green residential energy transition from traditional solid fuels (biomass and coal) to clean and high-efficient energy sources is critical for rural sustainable development. However, little attention has been paid to estimate panel data discrete choice models to analyze the dynamic behavior information of individual households in the process of energy transition. Hence, this paper investigates green residential energy transition using a panel dataset from 3308 rural households in eight provinces of China in 2015 and 2018. The results show that although traditional solid biomass still plays a dominating role in rural residential energy choice, fuel switching from solid fuels to modern clean energy alternatives is taking place. Off-farm employment plays an important role in the transition towards more sustainable energy sources, as households with off-farm employed heads and higher off-farm income level are highly likely to choose superior energy alternatives other than traditional biomass. Besides, the educational level of the household head and household location are also important influencing factors of household residential energy choices. Based on these findings, this paper suggests that job creation in non-farm sectors should be given a priority in future policy design to promote rural green energy transition in residential sector. North-south differences should be taken into account, whereas more policy options for optimizing biomass energy use should be explored.

Has the Development of Broadband Infrastructure Improved Household Energy Consumption in Rural China?

Digital infrastructure plays a crucial role in shaping household energy consumption behaviors, especially in the context of achieving carbon peaking and carbon neutrality goals in China. In this study, we explored the impact of broadband infrastructure on household energy consumption and clean fuel choices, using data from the China Health and Retirement Longitudinal Study (CHARLS). In a difference-in-differences (DID) framework, our findings indicate that the promotion of broadband infrastructure significantly increases rural household energy consumption and facilitates a transition to cleaner energy sources. Notably, households with better socioeconomic status, particularly those with higher education and income levels, benefit more from enhanced broadband internet access. Mechanism analysis reveals that the “Broadband China” policy can positively influence household consumption through several potential channels: fostering technological innovation, enhancing energy efficiency in cities, and improving overall environmental awareness. These findings contribute to our understanding of the role of broadband infrastructure in shaping household energy consumption patterns and have important implications for policymakers, particularly in the context of sustainable development and energy transition efforts.

Physical, socio-psychological, and behavioural determinants of household energy consumption in the UK

Determining which attitudes and behaviours predict household energy consumption can help accelerate the low-carbon energy transition. Conventional approaches in this domain are limited, often relying on survey methods that produce data on individuals’ motivations and self-reported activities without pairing these with actual energy consumption records, which are particularly hard to collect for large, nationally representative samples. This challenge precludes the development of empirical evidence on which attitudes and behaviours influence patterns of energy consumption, thus limiting the extent to which these can inform energy interventions or conservation programs. This study demonstrates a novel methodology for estimating energy consumption in the absence of actual energy records by using a large, publicly available data set of energy consumption in the UK. We develop a predictive model using the Smart Energy Research Laboratory (SERL) data portal (with records from nearly 13,000 UK households) and then use this model to predict energy consumption (both electric and gas) for a sample of 1,000 UK householders for which we separately collect over 200 variables relating to climate change attitudes and practices. Our approach uses a set of over 50 independent variables that are shared between the data sets, allowing us to train a model on the SERL data and use it to analyse the relationship between energy consumption and the opinions, motivations, and daily practices of survey respondents. Results show that electricity consumption is influenced by a broader range of factors compared to gas. Household energy use is best explained by physical dwelling characteristics, socio-demographic variables, and certain behavioural and attitudinal measures. Notably, pro-environmental attitudes, frugality, and conscientiousness correlate with lower energy use, while income and consumerism are linked to higher consumption. We discuss how these findings can inform efforts to decarbonise home energy use in the UK.

Empowering energy conservation: A prototype of household energy consumption monitoring based on Internet of Things

Abstract One of the obstacles to control household energy consumption is unavailability electricity instruments/tools to monitor electricity energy consumed by appliances causing a lack of awareness of household energy saving. Energy meter installed on buildings only show electricity consumption in total. This study aims to design and create a prototype system that can monitor and control the electrical appliances usage utilizing the Internet of Things (IoT)-based electronic devices where electricity usage data is stored in Firebase real-time database. The methods carried out are arranged in several stages, starting from data collection, design, configuration, and implementation. The tools used in this research will utilize the ESP8266 as the microcontroller, the PZEM-004T as current sensor and the Blynk as the web server. The tool will send current voltage (Volt (V)) and capacity (Watt (W)) data and then converted to energy and also the costs incurred from electricity usage based on the basic electricity tariff of National Electricity Company (PLN) and the results can be seen directly through IoT (i.e. smartphone, PC, laptop). The accuracy of power measurement by the system is 97.4% compared to measurement used by watt-checker. The results of this study show that an electrical power monitoring system makes people easier to monitor electrical power consumption by appliances used. The pilot measurement shows that a unit system can monitor electricity usage for four units of electronic equipment i.e. water pump, Television, fridge, electrical iron, rice cooker, etc. precisely and succeeded to control and increase energy saving awareness to reduce electricity consumption at the end.

Power Control for Household Energy Storage Inverter With Smooth Mode Transition

Power Control for Household Energy Storage Inverter With Smooth Mode Transition

Household Energy Poverty in European Union Countries: A Comparative Analysis Based on Objective and Subjective Indicators

The study aims to assess household energy poverty in European Union (EU) countries, comparing them based on the Objective Energy Poverty Index and the Subjective Energy Poverty Index. The Objective Energy Poverty Index is derived from indicators such as energy expenditure share, risk-of-poverty rate, and electricity prices. The Subjective Energy Poverty Index includes indicators such as the inability to keep the home adequately warm, arrears on utility bills, and bad housing conditions. Both indices aggregate the indicators mentioned above using equal and non-equal weighting approaches. The analysis uses country-level data from 2019 to 2023 sourced from Eurostat. The findings indicate considerable variation in household energy poverty across the EU, with more pronounced inequalities in subjective indicators than objective ones. Additionally, the study reveals a weak correlation between the Objective Energy Poverty Index and the Subjective Energy Poverty Index, leading to differing country rankings based on these indices. However, the choice of weights in constructing the energy poverty indices does not significantly impact a country’s energy poverty ranking. The paper also identifies countries where household energy poverty decreased in 2023 compared to 2019 and those where it increased. Regarding the Subjective Energy Poverty Index, Croatia and Hungary showed the most notable improvement in their rankings among European countries, while France, Germany, and Spain deteriorated their positions. According to the Objective Energy Poverty Index, Bulgaria, Croatia, Portugal, and Spain demonstrated the most significant improvement, whereas Greece experienced a considerable decline.

Enhancing climate resilience of vulnerable women in the Global South through power sharing in DC microgrids

Many women of remote communities in the Global South (GS) are highly vulnerable to natural disasters caused by climate change, due to their low adaptive capability. Inclusion of gender considerations has been emphasized in national adaptation plans and initiatives aimed at reducing vulnerability. However, the potential of low-cost electricity-based solutions in promoting climate change adaptation is largely unexplored. In countries of the GS, remote communities have adopted stand-alone solar home systems, which are often inefficient and require significant investments for upgrading. In this work, a model of an off-grid DC microgrid with distributed generation and storage is proposed, allowing individual households to obtain extra energy through neighborhood-level prosumer power sharing. The benefits of power sharing are evaluated through the development of a mixed-integer linear program using load requirements based on the World Bank's Multi-Tier Framework for household energy access. The results show that households with prosumer power sharing can access more than 60 % extra energy compared to their stand-alone status with up to 34 % reduction in Levelized Cost of Energy. Access to additional energy can allow vulnerable households to access energy services in the household to potentially improve climate change adaptation opportunities in agriculture, livelihood and educational sectors, among others.

Continuously Learning Prediction Models for Smart Domestic Hot Water Management

Domestic hot water (DHW) consumption represents a significant portion of household energy usage, prompting the exploration of smart heat pump technology to efficiently meet DHW demands while minimizing energy waste. This paper proposes an innovative investigation of models using deep learning and continual learning algorithms to personalize DHW predictions of household occupants’ behavior. Such models, alongside a control system that decides when to heat, enable the development of a heat-pumped-based smart DHW production system, which can heat water only when needed and avoid energy loss due to the storage of hot water. Deep learning models, and attention-based models particularly, can be used to predict time series efficiently. However, they suffer from catastrophic forgetting, meaning that when they dynamically learn new patterns, older ones tend to be quickly forgotten. In this work, the continuous learning of DHW consumption prediction has been addressed by benchmarking proven continual learning methods on both real dwelling and synthetic DHW consumption data. Task-per-task analysis reveals, among the data from real dwellings that do not present explicit distribution changes, a gain compared to the non-evolutive model. Our experiment with synthetic data confirms that continual learning methods improve prediction performance.

Unveiling the relation between household energy conservation and subjective well-being: Insights from structural equation modeling

Electricity is essential to Pakistan's economy and, it helps to improve the living standards of people. However, the state has long suffered from problems like imbalances in the supply of energy, excessive energy use, and frequent power outages. The present study focused on investigating the relationship between energy-saving behavior, consumption, and subjective well-being in households in urban areas of Punjab, Pakistan. Using Structural Equation Modeling (SEM), the research analyzed how different features of electricity use and energy-saving practices affect the quality of life for families in the region. According to research findings, a strong intention to save electricity is also linked to enhanced subjective well-being. When families have an intention toward conserving energy, they feel a more positive change in overall households' subjective well-being while electricity consumption which is measured in terms of electricity bills, negatively affects the household well-being. The current research concludes that some families can experience a financial benefit from lower energy bills, and others would need assistance switching to more environment-friendly energy habits. Governments must adopt price-control mechanisms and electricity subsidies for low-income groups to encourage ethical energy usage and well-being, while also undertaking awareness programs aimed at a varied audience.

Exploring attitudes and behavioral patterns in residential energy consumption: Data-driven by a machine learning approach

The present study focuses on two main objectives: firstly, to clarify the mechanisms by which attitudes impact behavioral changes related to household energy consumption, and secondly, to offer valuable insights to enhance the understanding of residential energy usage through a novel technique called Support Vector Regression (SVR). This method employs several feature space transformations to convert nNar relationships into linear ones. The results highlight the crucial role of psychological factors in determining energy consumption behaviors, demonstrating that cognitive factors significantly influence attitudes and behavioral patterns. The findings show that psychological variables have a major role in determining how people consume energy, with cognitive variables having a particularly large impact on attitudes and behavior patterns. Our findings demonstrate the superior performance of Support Vector Regression (SVR) with radial basis function kernels over traditional predictive models, with a prediction accuracy of 93.7 % for changes in behavior patterns (CHP) and 94.4 % for changes in attitudes (CHA). These results highlight the value of applying cutting-edge machine-learning approaches to create precise models for comprehending and directing energy-saving actions. The policy implications suggest that reducing cognitive barriers can significantly encourage energy-saving behaviors and contribute to a comprehensive approach for energy-efficiency initiatives

Mobilizing carbon offsetting to reduce energy cost burdens: a new approach for calculating and monetizing the offset value of energy efficiency upgrades to low-income housing

Energy-inefficient buildings are a major driver of climate change. The aging, energy inefficient housing occupied by low-income households in the US and other high-income countries is a driver of notable environmental and health disparities as well. Public policies for alleviating the financial and health burdens of energy inefficient housing have existed for decades, but fallen short of reaching most households in need. This paper proposes a promising new approach to filling gaps left by public programs — one that relies on mobilizing the tools of private governance, carbon offsetting and the voluntary carbon market (VCM) to finance energy upgrades for low-income households. We develop a new and readily applicable methodology for calculating energy and carbon savings from efficiency upgrades based on readily accessible publicly available data. Tailored to the needs of estimating energy and carbon savings from efficiency upgrades to low-income units, rentals in particular, this methodology can be fruitfully deployed in monetizing the carbon savings from efficiency driven reductions in household energy use. Specifically, we propose packaging the emission reductions generated through energy savings as carbon offsets, then selling these offsets on the voluntary carbon market to generate financing for energy upgrades to low-income homes not served by public energy efficiency programs. Given the multiple economic and health co-benefits from low-income energy upgrades, we expect that carbon offsets generated through such upgrades will be attractive to many corporate and institutional offset buyers, particularly those who seek to fulfill climate commitments while also advancing economic and human development in their host communities.

Optimizing smart home appliance energy monitoring using Factorial Hidden Markov Models for Internet of Behavior

The energy demand which is increasing globally strongly underlines the necessity of solutions that will improve power efficiency. This study addresses the research problem of enhancing energy management in smart homes through Non-Intrusive Load Monitoring (NILM), a method that enables consumers and companies to optimize their energy usage by disaggregating total household energy consumption into specific appliance-level data. The research focuses on the creation and establishment of an Internet of Behavior (IoB) model that encourages NILM to develop more energy-efficient and clever residential buildings as its main goal. The Factorial Hidden Markov Model (FHMM) is employed as the core methodology in this NILM process, facilitating the disaggregation of overall household energy usage into individual appliance consumption patterns. This method was used over the known Reference Energy Disaggregation Dataset (REDD) for comparing the actual energy consumption of the appliances with the projected estimates. The results are evidence that the FHMM method is an energy disaggregation technique that can provide important information about the customer usage of energy and, hence will help in the strategic shifting of high-energy appliances to non-peak hours for the user. This study's main value lies in the new application of IoB-based NILM methods to increase energy efficiency, which is the real solution for energy costs and the grid peak load problem. The results of the proposed method are compared with other methods and real data. The comparisons show the acceptable response of the proposed method when it is compared with the real data and is better than another method.

LightGBM-, SHAP-, and Correlation-Matrix-Heatmap-Based Approaches for Analyzing Household Energy Data: Towards Electricity Self-Sufficient Houses

The rapidly growing global energy demand, environmental concerns, and the urgent need to reduce carbon footprints have made sustainable household energy consumption a critical priority. This study aims to analyze household energy data to predict the electricity self-sufficiency rate of households and extract meaningful insights that can enhance it. For this purpose, we use LightGBM (Light Gradient Boosting Machine)-, SHAP (SHapley Additive exPlanations)-, and correlation-heatmap-based approaches to analyze 12 months of energy and questionnaire survey data collected from over 200 smart houses in Kitakyushu, Japan. First, we use LightGBM to predict the ESSR of households and identify the key features that impact the prediction model. By using LightGBM, we demonstrated that the key features are the housing type, average monthly electricity bill, presence of floor heating system, average monthly gas bill, electricity tariff plan, electrical capacity, number of TVs, cooking equipment used, number of washing and drying machines, and the frequency of viewing home energy management systems (HEMSs). Furthermore, we adopted the LightGBM classifier with ℓ1 regularization to extract the most significant features and established a statistical correlation between these features and the electricity self-sufficiency rate. This LightGBM-based model can also predict the electricity self-sufficiency rate of households that did not participate in the questionnaire survey. The LightGBM-based model offers a global view of feature importance but lacks detailed explanations for individual predictions. For this purpose, we used SHAP analysis to identify the impact-wise order of key features that influence the electricity self-sufficiency rate (ESSR) and evaluated the contribution of each feature to the model’s predictions. A heatmap is also used to analyze the correlation among household variables and the ESSR. To evaluate the performance of the classification model, we used a confusion matrix showing a good F1 score of 0.90. The findings discussed in this article offer valuable insights for energy policymakers to achieve the objective of developing energy-self-sufficient houses.

Forecast of Commodity Energy Consumption and Structure of Rural Households in Sichuan Province

With the growth in energy consumption, problems such as environmental pollution and resource depletion are becoming more and more prominent, and the sustainable use of energy resources and sustainable economic and social development can be achieved only through scientific forecasting and effective management. Forecasting the changing trend of energy consumption structure can guide the government and society to take corresponding measures for environmental protection and sustainable development, and reduce the negative impact of energy consumption on the environment. Based on the current development situation of commodity energy in rural households in Sichuan Province and related consumption data, this paper uses the grey prediction model and Markov model to predict the consumption and consumption structure of commodity energy in rural households in Sichuan Province. It is found that among the four types of commodity energy consumed by rural households in Sichuan Province, the consumption of electricity shows exponential and growth, and it is expected that the consumption of electricity by rural households in Sichuan Province from 2022 to 2025 will be 3,316,166, 3,582,203, 3,886,622, and 4,216,240 tonnes of standard coal, respectively. Meanwhile, using Markov chain to predict the consumption structure, it is found that under the no-planning constraint scenario, the share of energy consumption of electricity and natural gas is increasing, while the share of energy consumption of coal and liquefied petroleum gas (LPG) will be decreasing, and the shares of energy consumption of coal, electricity, natural gas, and LPG will be 1.97 per cent, 75.82 per cent, 26.36 per cent, and 0.02 per cent, respectively, by 2030.

Trends of forest cover and prospects of Great Ethiopian Renaissance Dam (GERD) to Ethiopia and the downstream countries

The need for forest products, agricultural expansion, and dependency on biomass for the household energy source has largely influenced Ethiopia’s forest resources. Consequently, the country lost its forest resources to less than 6% until the millennium. In this study, quantitative and qualitative historical data analysis was employed to understand the socioeconomic benefits of large dam construction to Ethiopia and downstream countries. Moreover, remotely sensed data was also used to analyze the trends of vegetation cover change in the Nile catchment since the commencement of the dam; focusing on areas where there are high settlement and urban areas. It was identified that Ethiopia has one of the lowest electricity consumption per capita in Africa; about 91% of the source of household energy supply depends on fuelwood today and more than 55.7% of the population does not have access to electricity. The normalized difference vegetation index result shows an increment of vegetation area in the Nile catchment and a reduction of no vegetation area from 2011–2021 by 37.1%; which is directly related to the protection of the dam catchment for its sustainability in the last decade. The hydroelectric dam construction has prospects of multi-benefit to Ethiopia and downstream countries either through the direct benefit of hydropower energy production, related socioeconomic values, and reducing risks of destructive flood from Ethiopian highlands. Generally, it explains the reason why to not say ‘No’ to the reservoir as it is an ever more vital tool for fulfilling growing energy demand and supporting ecological stability.