End-use Energy Research Articles

Residential Load Forecasting Based on Long Short-Term Memory, Considering Temporal Local Attention

Accurate residential load forecasting is crucial for the stable operation of the energy internet, which plays a significant role in advancing sustainable development. As the construction of the energy internet progresses, the proportion of residential electricity consumption in end-use energy consumption is increasing, the peak load on the grid is growing year on year, and seasonal and regional peak power supply tensions, mainly for household electricity consumption, grow into common problems across countries. Residential load forecasting can assist utility companies in determining effective electricity pricing structures and demand response operations, thereby improving renewable energy utilization efficiency and reducing the share of thermal power generation. However, due to the randomness and uncertainty of user load data, forecasting residential load remains challenging. According to prior research, the accuracy of residential load forecasting using machine learning and deep learning methods still has room for improvement. This paper proposes an improved load-forecasting model based on a time-localized attention (TLA) mechanism integrated with LSTM, named TLA-LSTM. The model is composed of a full-text regression network, a date-attention network, and a time-point attention network. The full-text regression network consists of a traditional LSTM, while the date-attention and time-point attention networks are based on a local attention model constructed with CNN and LSTM. Experimental results on real-world datasets show that compared to standard LSTM models, the proposed method improves R2 by 14.2%, reduces MSE by 15.2%, and decreases RMSE by 8.5%. These enhancements demonstrate the robustness and efficiency of the TLA-LSTM model in load forecasting tasks, effectively addressing the limitations of traditional LSTM models in focusing on specific dates and time-points in user load data.

Developments in caloric measurements, materials, and devices at Calorics 2024

Abstract Heating and cooling combined constitute the world’s largest form of end-use energy and the largest source of carbon emissions. It is therefore interesting to explore heat pumps based on caloric materials, which offer promising and environmentally friendly alternatives to gas combustion and vapor compression. The possibility of replacing these traditional methods of heating and cooling motivates the current research on caloric materials and devices. Here, we report the latest developments from the second biennial Calorics conference. Graphical abstract Highlights Caloric materials offer environmentally friendly alternatives to traditional heating and cooling technologies, addressing global energy and carbon emission challenges. The latest developments on caloric measurements, materials and devices were presented at the second biennial Calorics conference in Cambridge (UK). Discussion How can caloric technologies be made more energy efficient to ensure they outperform traditional vapor-compression systems? What are the most significant barriers to scaling caloric materials and devices from lab-scale prototypes to commercial applications? Could integrating caloric technologies with renewable energy sources, such as solar or waste heat recovery, offer sustainable solutions for heating and cooling?

Optimized Smart Home Energy Management System: Reducing Grid Consumption and Costs through Real-Time Pricing and Hybrid Architecture

Utility authorities utilize various methods to promote end-user energy conservation, including higher tariff rates and demand response (DR) strategies. This paper investigates an Optimized Smart Home Energy Management System (OSHEMS) designed to minimize grid dependence and energy bills while ensuring reliable load delivery. A hybrid architecture prototype was implemented, integrating a photovoltaic (PV) array, battery storage, and the electrical grid. The system combines Maximum Power Point Tracking (MPPT) solar chargers and Pure Sine Wave (PSW) inverters for efficient energy management. The Home Energy Management Whale Optimization Algorithm (HEMWOA) was employed to optimize energy usage and achieve cost reduction while enhancing user comfort. Real-time pricing (RTP) tariffs incentivizing flexible energy consumption during peak hours were incorporated. OSHEMS manages, schedules, and monitors energy sources and appliances, determining the optimal consumption mix. Experimental results demonstrate a significant decrease in grid reliance (46.6%) and energy costs (57.7%) compared to non-scheduling scenarios. These findings highlight the potential of OSHEMS in promoting sustainable and cost-effective energy consumption in smart homes.

UAV-BIM-BEM: An automatic unmanned aerial vehicles-based building energy model generation platform

Many existing and aging buildings were in urgent need of upgrades and renovations, but lack accurate geometric and energy consumption information. This study introduced an unmanned aerial vehicles-based (UAV-based) automated platform for building information modellings (BIM) to generate builidng energy modellings (BEM), which was called the UAV-BIM-BEM platform. The platform enabled the creation of energy models for existing buildings to estimate energy consumption and perform retrofit analysis. A four-story office building in Hong Kong, with dimensions of 52.3 m in length, 14.3 m in width, and 16.5 m in height, was selected as the case study building. Firstly, using UAV oblique photography techniques and path planning algorithms, the building images were transformed into a detailed 3D point cloud model. The point cloud model was provided to Revit to generate the BIM model in IFC format. The IFC file containing the geometric information of the building was then supplied to the AutoBPS-BIM tool, resulting in the generation of the BEM model in EnergyPlus to simulate detailed end-use energy consumption. The annual electricity use intensity (EUI) of the building was estimated to be 145.05 kWh/m2. Lastly, the generated energy model was used to evaluate the energy-saving potential of three different measures. The results showed that the electrical energy savings from cooling equipment, lighting systems, and window replacement are 17.22 %, 6.91 %, and 6.61 %, respectively. This research demonstrated that the UAV-BIM-BEM platform has great potential in BEM modeling for existing buildings and building energy retrofitting.

When differentiated carbon tax policy meets LBD of renewable energy and electrification of energy end-use: Policy implications of sectoral differentiation of carbon productivity and carbon emission

The impact of environmental policy stringency on energy efficiency innovation has been studied well without considering renewable energy storage and electrification to meet demand for energy structural transition in China. Based on these past studies, this paper introduces the CGE model to analyze carbon tax stringency via carbon productivity by accounting for the electrification and learning-by-doing effect of renewable energy. Through comparing the performance of the uniform and differentiated carbon tax, it is found that the differentiated carbon tax always has a worse environmental performance in reducing carbon emissions due to less energy efficiency improvement than uniform one. While uniform carbon tax at stricter level gains more economic and environmental benefits on sectoral and overall aspects in long term because of greater improvement on sectoral carbon productivity in energy-intensive industries. Furthermore, these findings at a large level reveal the intricate interaction among carbon tax, sectoral carbon productivity, renewables-related subsidy, electrification, and the learning-by-doing of renewables for accelerating the energy structural transition and simultaneously achieving the coordination between economic growth and environmental protection.

Modeling of heat pumps load profiles for power systems integration

Heat pumps (HPs) are one of the most efficient heating technologies; their mass adoption will be required to decarbonize energy systems. However, to do so will require a better understanding of how they will impact electric grid load. Methods are needed to estimate not just their peak demand but also their impact on hourly load profiles. In this paper, we propose two methods, using easily accessible data, for estimating future hourly load profiles following the adoption of large populations of residential HPs. The first method uses feeder load data disaggregation while the second method uses annual space heating end-use energy consumption, both taking into account the temperature dependencies on coefficient of performance and output heat capacity. A case study based on data from Summerside, PE, Canada, is used to demonstrate and evaluate the two methods.

The impact of energy prices in decarbonizing buildings’ energy use in the EU27

The recent surge in end-use energy prices in the EU has intensified the debate on how price signals influence the decarbonization of the building sector, a key contributor to global energy consumption and emissions. This study investigates the role of energy price signals in driving the decarbonization of the EU-27 building sector using the Invert/EE-Lab model to simulate various scenarios up to 2050. The analysis examines the elasticity of energy demand, particularly how different price scenarios impact investment decisions in energy-related technologies. The findings reveal that a 10 % increase in energy prices can lead to a reduction in energy demand by up to 3 %, depending on the scenario. Furthermore, price elasticity varies across different policy and price settings, with stricter regulations resulting in lower elasticity. The results underscore that while stringent regulatory measures can achieve significant energy savings and reduce greenhouse gas emissions, they also limit the responsiveness to price signals, with heat pump shares reaching 47–86 % in high-efficiency scenarios. Conversely, more moderate policy measures increase price elasticity, leading to 15–55 % heat pump shares. This study highlights the importance of better understanding price elasticity in energy modeling to project better the outcomes of policy interventions aimed at achieving a low-carbon energy system.

Medium and long-term energy demand forecasts by sectors in China under the goal of “carbon peaking & carbon neutrality”: Based on the LEAP-China model

Energy is a critical material foundation for sustainable economic and social development and national security, and it is of great significance to explore China's medium and long-term energy demand to realize the “dual-carbon” goal. Based on the current state of economic and social development and energy consumption, the LEAP-CHINA model is constructed to create baseline scenario, structural adjustment scenario, technology abatement scenario, and comprehensive scenario to forecast China's total energy demand, end-use industry subsectors, end-use energy subvarieties, processing and conversion sectors from 2022 to 2060. The results show: (1) China's total energy demand showed a “rapid increase before 2039, a slow increase between 2039 and 2049, and a gentle decrease after peaking in 2049". (2) The contribution rate of energy conservation and emission reduction was comprehensive scenario > technology abatement scenario > industrial adjustment scenario > baseline scenario > energy adjustment scenario. (3) Industry was the largest energy demand industry, and the rest of the industries' energy demand share was decreasing. (4) There is a definite trend towards power system decarbonization and energy system electrification, with non-fossil energy power generation anticipated to increase to 78%–82 % in 2060. Finally, it proposes countermeasures for China's energy development in the medium and long-term.

The role of electrification and the power sector in U.S. carbon neutrality

The United States has pledged to achieve net-zero greenhouse gas emissions by 2050. We examine a series of net-zero CO2 scenarios to investigate the impact of advanced electrification of end-use sectors on the dynamics of America's net-zero transition through 2050. Specifically, we use an integrated assessment model, GCAM-USA, to explore how advanced electrification can influence the evolution of the electricity system in pursuit of net-zero. State-level resolution for end-use demand sectors and energy transformation is a key feature of GCAM-USA that allows for elucidation of the variation in end-use electrification across states. All scenarios in this study are designed to be consistent with the modeling protocol for the Energy Modeling Forum Study 37 model inter-comparison project. Our scenarios show the scale of transformation in the power sector with average annual capacity additions reaching 121-143 GW/year and 172-190 GW/year in 2050 net-zero CO2 scenarios and 2045 net-zero CO2 scenarios, respectively, in the 2040s — approximately three to five times the 2021-2023 average. In 2050 net-zero CO2 scenarios, electrification rates in 2050 range from 15-48 % for transportation, 65-83 % for buildings, and 20-38 % for industry. If net-zero CO2 is achieved in 2045, transportation, buildings, and industry are 27-53 %, 78-84 %, and 41-53 % electrified by 2050, respectively. Advanced electrification of end-use sectors can reduce the magnitude of reliance on negative emissions by driving down residual positive emissions by mid-century. Altogether, our results demonstrate that a net-zero transition in the United States will require deep and rapid structural changes to the energy system.

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

Socioeconomic factors influencing residential occupancy trends during and post COVID pandemic

The residential building sector accounts for 22% of end-use energy consumption in the United States. Despite the strong influence of occupants’ behavior on energy consumption patterns in residential buildings, the impact of households’ socioeconomic background on occupancy is not well understood in the wake of the COVID-19 pandemic and its aftermath. This study aims to analyze the changes in occupancy patterns of residential buildings in the United States during and after the pandemic (2020–2022) using 14 socioeconomic variables. The American Time Use Survey (ATUS) data are used to define occupancy patterns; then correlation and regression analyses are applied to determine the most significant variables impacting the hours when members are at home. Results suggest employment status and household income level are the most significant variables predicting hours at home. Those under 25 years of age, low-income households, unemployed, and those identifying as Hispanic have most quickly returned to prepandemic (2018–2019) occupancy patterns. The results indicate that post pandemic (2022), occupancy patterns continue to change for those under 55, employed, and middle- and high-income groups, and thus must be monitored moving forward as they continue to evolve. These results are critical to help support ongoing electrification of homes and decarbonization of the electric grid.

Analysis of energy supply, energy policies, and the final energy end-use consumption of the residential sector in Ethiopia

The residential sector in Ethiopia heavily relies on biomass for cooking, using inefficient cookstoves. In order to assess energy policies and decision-making for better economic development, it is essential to have final energy consumption by end-use. However, there is a lack of readily accessible data on residential energy end-use. Our study fills this gap by using data collected from surveys of 590 urban households in Ethiopia, estimating their energy end-use consumption, and analyzing their determinants. The annual final energy consumption per household is about 7.2 MWh, where 90 % is for cooking, baking, tea/coffee boiling end-uses, and only 2.3 % for lighting. The analysis reveals that income has the strongest effect on energy consumption for Injera baking and on miscellaneous end-uses, both directly and partly indirectly as a mediating variable. The study highlights the importance of end-use consumption data to plan energy efficiency, mix technology options, and make suitable policy interventions.

Towards effective implementation of the energy efficiency first principle: a theory-based classification and analysis of policy instruments

The energy efficiency first (EE1st) principle, embedded in the European Union's (EU) recast Energy Efficiency Directive, aims to prioritise cost-effective energy efficiency solutions over new energy supply infrastructure. These solutions encompass not only end-use energy efficiency, but also, notably, demand-side flexibility. This research bridges the gap between the theoretical underpinnings of the principle and practical policy implementation. Drawing on market failure theory, it identifies an inherent bias in EU energy markets in favour of energy supply infrastructure. By highlighting the correction of market failures – from externalities to transaction costs – through targeted policy instruments, it presents a theoretical policy intervention logic for EE1st. Based on this, it offers a set of twenty-nine established and emerging policy instruments linked to specific market failures, thus providing a systematic roadmap for EE1st application. The study concludes that levelling the playing field between energy efficiency and energy supply requires a broad policy response. This extends beyond traditional energy efficiency policy of standards and public financing, and includes instruments such as electricity market design, remuneration schemes for network operators, and emissions pricing.

EPL-based medium- and long-term end-use energy service demand forecasting model for parks

EPL-based medium- and long-term end-use energy service demand forecasting model for parks

Cost Benefits of Net Zero Energy Homes in Australia

This paper presents a systematic analysis of energy savings and cost benefits associated with several options for integrating energy efficiency and renewable energy technologies. The primary goal of this study is to assess the cost-effectiveness of achieving optimal net-zero energy (NZE) designs for residential buildings in Australia. Specifically, the analysis combines a series of sensitivity analyses and multi-objective optimizations to account for a wide range of design strategies for detached homes in four cities representing different Australian climates. The results indicate that not only are NZE designs technically feasible for all the considered Australian cities, but they are also highly cost-effective. This cost-effectiveness is attributed to the lower installation costs of rooftop PV systems as well as the beneficial interactive effects of proven energy efficiency strategies. Indeed, it is found that the deployment costs of rooftop PV systems can be recovered in less than 4 years. Moreover, the addition of thermal insulation in walls and ceilings can reduce both HVAC capacities and annual energy end-use by up to 59%. Based on an optimization-based design, NZE homes in Australia can have lower construction costs and, ultimately, lower life cycle costs than dwellings built to meet current energy efficiency standards based primarily on stringent building envelope thermal performance.

Identification of carbon responsibility factors based on energy consumption from 2005 to 2020 in China

In the process of rapid economic development, the relationship between energy consumption and economic development is close and the coupling relationship is complex. The study proposes a method for the assessment of integrated energy consumption factors. In particular, production and consumption driving factors are expressed in relation to the growth of primary energy consumption responsibility in the energy supply chain system. Specifically, based on previous research, the study builds a LMDI decomposition model based on the primary energy consumption responsibility - embodied energy (PECREE) formula. By analyzing the impact of changes in the final commodity quantity, value-added allocation factor, energy intensity, end-use energy consumption structure and primary energy consumption responsibility conversion factor on primary energy consumption responsibility, the study systematically reveals the linkage relationship between product production and consumption on energy consumption changes, and reveals the related driving mechanism of primary energy consumption responsibility growth. We choose China's energy supply system from 2005 to 2020 as a case study, focusing on the impact of changes in economic sectors on primary energy consumption responsibility, and provide a supplementary explanation from the commodity consumption side. The results show that the change of final use quantity is the most important factor causing the increase of primary energy consumption responsibility, but the impact is decreasing year by year. The impact of energy intensity change on primary energy consumption responsibility is mainly dominated by economic sectors with high energy consumption and high energy intensity. The impact of value-added allocation factor requires systematic consideration of energy intensity of economic sectors. The increase of the proportion of electricity in the end-use energy consumption structure of the economic sector promotes the growth of its primary energy consumption responsibility. The reduction of primary energy consumption responsibility conversion factor for electricity is one of the factors that inhibits the growth of primary energy consumption responsibility. Finally, we believe that while meeting the needs of social development and production and life of the people, China is already on the road of adjustment and transformation of its industrial structure and energy structure, and will continue to make progress in the future.

Does energy poverty influence decarbonisation through electrification of the heating Sector?

Cleaner end-uses of energy, including electrified heating, represent a cornerstone of a decarbonised energy transition. On the other hand, many governments have adopted measures to mitigate energy poverty and facilitate access to modern energy services, including heating. Both objectives may be interrelated, since energy poor people are less likely to use cleaner (and costlier) heating fuels. This paper analyses the impact of energy poverty on the decision to use different heating sources and identifies whether the events of COVID and the Ukraine war have affected this decision. Thus, a multinomial probit model is estimated using information from a large database of Spanish households in 2019, 2021 and 2022. The results show that being energy poor increases the probability to use carbon-intensive energy sources for heating compared to electricity, and that COVID and the war in Ukraine have affected this relationship. The increase in energy poverty over those years has negatively affected the decarbonisation goal with respect to heating choices. The influence of those events on the probability to use electric heating depends on some household and dwelling features. Therefore, mitigating energy poverty increases the welfare of energy poor people while supporting the choice of cleaner heating.

Moth Flame Optimisation for optimal power flow in a power system with Static var compensator

It consequently becomes imperative in the current transmission networks to make full use of existing resources and to rapidly transition to renewable energy alternatives. Electrical energy end users also benefit from effective resource utilisation since it lowers the price they pay for electricity. In this work, we offer a multi-objective optimum power flow (OPF) for an integrated gearbox network where FACTS devices are present. The paper's originality lies in the use of a multi-objective function. Minimising voltage variation, power loss, and negative social welfare (NSW) are all part of the objective function. The abatement of loss and NSW guarantees the lowering of per-unit pricing of power at the consumer end resulting in improved customer satisfaction. The problem was fixed using a FACTS Static var compensator. The theory was tested using an IEEE 30 bus network. The objective function has been optimised using the Mouth Flame Optimisation Algorithm. Detailed presentations, comparisons, and analyses of the collected findings have been made.

Experimental study on a two-stage absorption thermal battery with absorption-enhanced generation for high storage density and extremely low charging temperature (∼50 °C)

Absorption thermal battery (ATB) is a promising solution to balance the timing or intensity mismatch between low-grade renewable energy sources and end users, which can significantly alleviate the growing energy and environmental issues. However, the performance of the basic ATB needs to be improved, and there is still a lack of experimental research on advanced ATBs. This study proposes a two-stage ATB with absorption-enhanced generation to achieve high energy storage density (ESD) and extremely low charging temperature. A prototype is designed and manufactured, including the single-stage and two-stage modes. The experimental results indicate that the single-stage ATB is fully charged under a charging temperature of 90 °C with an energy storage efficiency (ESE) of 0.62 and an ESD of 137.3 kWh/m3 (357.2 kJ/kg). Under a charging temperature of 70 °C, the ESD of the single-stage ATB is only 62.7 kWh/m3 (163.1 kJ/kg), which can be greatly enhanced to 100.0 kWh/m3 (260.1 kJ/kg) by the two-stage ATB. The lowest charging temperature of the single-stage ATB is 60 °C, with an ESE of 0.31 and an ESD of 22.1 kWh/m3, while the two-stage ATB improves the ESE and ESD by 9.7% and 190.5%, respectively. Even under 50 °C, the ESE (0.33) and ESD (29.4 kWh/m3) of the two-stage ATB are higher than those of the single-stage ATB under 60 °C. The experiment results prove that the two-stage ATB has the advantages of higher ESD and lower charging temperature than the conventional ATB, which provides a promising option for low-grade renewable energy utilization.

The inscrutable baseline and the problem of attribution

This paper examines the problem of attribution in the evaluation of energy efficiency program impact. The methodological problem concerns the observability of consumer behavior under the baseline condition of no program intervention. The statistical solution to the problem, which entails randomized exposure of targeted individuals to program influence, is not a viable alternative in most applications. Randomized opt-in and randomized encouragement designs do not conform to this requirement because all targeted individuals are encouraged to participate in the program, resulting in negative exposure bias. Quasi-experimental methods which utilize non-targeted individuals or targeted nonparticipants as baseline surrogates are further subject to selection bias of unknown magnitude and direction. Valid attribution in the general case of unrestricted eligibility depends on prior knowledge of the determinants of measure adoption and program participation. In default of such knowledge, evaluators must rely upon structural assumptions that have no foundation in empirical science. On the other hand, established measurement and verification methods which exploit scientific knowledge of the determinants of end-use energy consumption should be utilized to obtain unbiased estimates of individual measure and gross program energy savings.