Personal Energy Consumption Research Articles

Dynamic optimization of personal exposure and energy consumption while ensuring thermal comfort in a test house

Owing to significant time spent indoors, indoor air quality (IAQ) and thermal comfort are critical to ensure occupants' well-being. Buildings already account for a considerable fraction of developed nations’ energy consumption, primarily for maintaining thermal comfort. Measures to improve IAQ can further increase the energy demand. Thus, optimizing IAQ, energy consumption, and thermal comfort is critical. This work presents a dynamic optimization model to investigate the complex and interdependent relationship between personal exposure to particulate matter (PM), thermal comfort, and energy consumption in a test house during typical cooking activities and intense holiday cooking. Surface deposition dominated PM removal for both scenarios (72–78 %). During optimization of the cost function with higher weightage for exposure, exfiltration became the primary PM removal mechanism due to the increased outdoor-indoor air change rate. However, this also increased air conditioning energy consumption. Adding a filter to the recirculation system and increasing the indoor set temperature can save energy while maintaining the same level of exposure reduction achieved via exfiltration alone. Simulations corresponding to higher outdoor temperatures demonstrated that increasing the indoor set temperature from 25°C to 27°C reduces exposure and energy consumption relative to the benchmark without considerable compromise to the comfort level. A high normalized exposure reduction results in an energy-efficient system but might not always translate to a desirable exposure reduction, thus indicating an energy-exposure trade-off.

Nexus between Personal Remittances, Financial Deepening, Urbanization, and Renewable Energy Consumption in Selected Southeast Asian Countries: Evidence from Linear and Nonlinear Assessment

This study examines the correlation between personal remittances, financial development, urbanization, and renewable energy utilization in Southeast Asian countries for the period 1991-2020. The study employs both linear and nonlinear analyses to examine the symmetric and asymmetric effects of these variables on renewable energy consumption. The results offer a plethora of significant insights. To commence, it is noteworthy that there exists an adverse correlation between financial deepening and the utilization of renewable energy, thereby challenging the prevailing notion that these two factors are positively interconnected. This demonstrates that the increasing financial growth in South Asian countries could potentially have adverse effects on the environment and hinder the implementation of renewable energy solutions. Furthermore, previous studies have demonstrated a positive correlation between urbanization and the utilization of renewable energy sources. The utilization of renewable energy technology is encouraged in cities due to factors such as population density, resource availability, and supportive legislation. Thirdly, personal remittances exhibit a varied impact, yielding favorable long-term consequences in Bangladesh and Pakistan, while conversely resulting in negative long-term effects in India and Sri Lanka. The findings indicate the necessity for governmental interventions aimed at mitigating the adverse effects of remittances, while simultaneously optimizing their positive influence on the utilization of renewable energy. Trade openness is positively correlated with long-term utilization of renewable energy, suggesting the potential for international trade in renewable energy resources. Nevertheless, it is worth noting that there is a negative correlation between trade liberalization and the utilization of renewable energy in the short term. This highlights the importance of implementing additional policy measures to address this issue. This study contributes to our comprehension of the intricate connections among personal remittances, financial deepening, urbanization, and energy consumption in Southeast Asian nations. It offers valuable insights for policymakers, researchers, and practitioners involved in advancing sustainable energy transitions and environmental sustainability.

Investigating correlates of personal and freight road transport energy consumption: A case study of England

In most countries worldwide, the transport sector is responsible for a large proportion of energy consumption, the emissions of which have adverse effects on the environment and human health. It is therefore important to understand the determinants of road transport energy consumption in an attempt to minimise these adverse effects. This paper examines the association which road transport energy consumption, for both personal and freight uses, has with a number of area-level factors, covering a host of socio-economic, built environment and travel mode choice variables. We considered England as our case study, using local authority level data. A random parameters multilevel regression model was utilised in order to accommodate the hierarchical structure of the data, with local authorities nested within major areas of England, and to address unobserved heterogeneity more fully. We paid a particular attention to understand the association between levels of active travel and road transport energy consumption, as this is less-understood. Most notably, gross disposable household income per capita had a positive association with personal road transport energy consumption, and the proportion of walking and cycling had a negative association with both personal and freight consumption. The analysis presented here may be useful in modelling the effect that anticipated changes might have on road transport energy consumption, for instance, new transport developments. In particular, local authorities may consider making a concerted effort at promoting active travel as this was found to be highly negatively associated with road transport energy consumption. As well as this, an insight into the disparity in transport energy consumption between geographical areas is provided, which may otherwise go unobserved.

Towards democratization of digital twins: Design principles for transformation into a human-building interface

While legacy building digital twins (DTs), such as building management systems, help building and facility managers to make informed decisions, the real end-users of buildings, i.e., occupants, have been considered as data informants without agency for access to and interaction with DT systems. This is due to the lack of multi-user interfaces for occupants to interact with building systems effectively with sufficient observability and controllability. The research on the design of multi-user interfaces has been limited to specialized services in public, shared spaces, or office-specific experiments and fails to produce detailed design principles. In this study, we explore the “democratization of DTs” as an occupant-building interface that invites occupants to monitor the building- and human-related data and make informed decisions. An occupant participatory approach was employed, consisting of a survey, an experience sampling method, and a co-design workshop, to understand occupants' current experiences with environmental controls and desired methods to interact with a DT system. The findings identified five interaction dimensions between occupants and a novel occupant-in-the-loop DT system that provides occupants with observability and controllability over building systems. Accordingly, we establish design principles for DT systems as an occupant-building interface that supports occupants’ dynamic contexts, intuitive controls, collaborative and balanced decision-making between personal comfort and energy consumption, as well as authority and data privacy.

Determinants of Employees’ Personal and Collective Energy Consumption and Conservation at Work

Energy conservation in public buildings is an important means towards reducing CO2 emissions worldwide and tackling climate change. In this context, employee behaviour has been recognised as a highly impactful factor that needs to be studied more thoroughly. In this study, we propose and investigate a behavioural model that can be utilised in energy-saving interventions in the workplace. Employing a questionnaire (N = 119 employees in three workplaces in EU countries), we identified two types of energy consumption behaviour at work: personal and collective actions. We further investigated the effect of six factors on employee willingness, as well as self-reported energy-saving habits and behaviour. We found that an employee’s profile (i.e., i. personal energy-saving norms, ii. emotional exhaustion/burnout, iii. collective energy-saving responsibility and efficacy, iv. awareness of energy wastage and knowledge of solution, v. personal comfort/comfort levels, vi. age, vii. gender, and viii. having children) determines energy-saving habits and behaviour, as well as affects willingness to alter it and to conserve energy at work. Employee willingness in turn directly affects energy-saving habits and behaviour at work. The proposed behavioural model can provide guidance towards applying energy conservation initiatives in the workplace. Behavioural interventions should accordingly primarily focus on improving personal energy-saving norms at work and be designed to be easy to follow and not overly demanding, time consuming, or pressuring. Moreover, to motivate collective energy-saving behaviours, interventions should focus on increasing employees’ collective energy-saving responsibility and efficacy, while respecting their personal comfort/comfort levels and their emotional exhaustion/burnout levels. Practical advice towards specific types of interventions is provided accordingly.

Personalized Energy Consumption Prediction of CAEVs With Personal Driving Cycle Selection

Personalized Energy Consumption Prediction of CAEVs With Personal Driving Cycle Selection

Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling

Analyzing the energy consumption for road entities and the corresponding driving behaviors are critical tasks for the realization of public traffic with a low energy cost and high efficiency. In this study, a personalized energy consumption analysis and prediction framework are proposed to estimate future energy consumption and the speed of a vehicle. An accumulation energy consumption index is predicted based on the features of the driving behavior. This approach is independent of the vehicle style, and it can play a critical role in the estimation of energy consumption as well as energy management for both petrol and electric vehicles. Three different energy-oriented driving behaviors are first identified and compared. It is shown that the vehicles with heavy energy usage have the characteristics of a higher speed, larger acceleration, larger headway space, and smaller headway time. The relationship between the energy consumptions and acceleration-deceleration characteristics are analyzed, and it is noted that the heavy energy users tend to perform acceleration maneuvers more frequently and with a longer period. Finally, a personalized joint time series modeling system based on the long short-term memory and a recurrent neural network is designed to jointly estimate the future energy consumption index considering different driving styles. It is found that the proposed personalized sequence prediction framework can generate more accurate results than the models that do not consider the energy cost levels and driving behaviors. The next-generation simulation data for free highway driving behaviors are used for the analysis and model evaluation.

A Study on the Factors Affecting China’s Direct Household Carbon Emission and Comparison of Regional Differences

As the world’s largest emitter of greenhouse gases, China has been attracting attention. In the global carbon emission structure, the proportion of household carbon emissions continues to increase, and it is necessary to focus on the issue of household emissions. Based on the perspective of the family sector and the comparison of urban–rural and interprovincial differences, this study makes a thorough and systematic analysis of the factors affecting direct household carbon emissions. The average carbon emission of urban households is higher than that of rural households. Both personal background and household energy consumption facility use have important impacts on household carbon emissions, and the degree of impact varies between urban and rural areas and between provinces. Reducing household carbon emissions and achieving a harmonious coexistence between man and nature are the common goals of the government and society. The government should explore the model of green sustainable development on the basis of ensuring the energy needs of residents. Residents should also further establish a low-carbon life concept and focus on the cultivation of low-carbon lifestyles.

Encouraging energy conservation at work: A field study testing social norm feedback and awareness of monitoring

The financial cost of personal energy consumption is substantial at the organizational level. Rarely do incentives for saving energy for the individual employee and organization align, making conservation a challenge. Here we perform a 12-week field experiment piloting two behavioral strategies: (1) social norms feedback and (2) awareness of energy monitoring, to encourage savings among 46 administrative staff at a university who were given the impression of participating in an energy quality study to reduce self-selection bias. Those in the social norms feedback condition used significantly less energy during the intervention (10% less energy) and follow up monitoring (11% less energy) phases compared to their baseline consumption. Moreover, these participants demonstrated that they learned more about their energy use than did those in the awareness monitoring condition. For policy makers and building mangers interested in the effectiveness of behavioral nudges in inducing energy savings, social norms feedback appears to be an effective energy savings nudge in an organizational setting where there are neither financial savings at-stake nor intrinsic motivation to conserve.

Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones

Air pollution and climate change are some of the main problems that humankind is currently facing. The electrification of the transport sector will help to reduce these problems, but one of the major barriers for the massive adoption of electric vehicles is their limited range. The energy consumption in these vehicles is affected, among other variables, by the driving behavior, making range a value that must be personalized to each driver and each type of electric vehicle. In this paper we offer a way to estimate a personalized energy consumption model by the use of the vehicle dynamics and the driving events detected by the use of the smartphone inertial sensors, allowing an easy and non-intrusive manner to predict the correct range for each user. This paper proposes, for the classification of events, a deep neural network (Long-Short Time Memory) which has been trained with more than 22,000 car trips, and the application to improve the consumption model taking into account the driver behavior captured across different trips, allowing a personalized prediction. Results and validation in real cases show that errors in the predicted consumption values are halved when abrupt events are considered in the model.

A computational model for designing energy behaviour change interventions

The conflicting evidence in the literature on energy feedback as a driver for energy behaviour change has lead to the realization that it is a complex problem and that interventions must be proposed and evaluated in the context of a tangled web of individual and societal factors. We put forward an integrated agent-based computational model of energy consumption behaviour change interventions based on personal values and energy literacy, informed by research in persuasive technologies, environmental, educational and cognitive psychology, sociology, and energy education. Our objectives are: (i) to build a framework to accommodate a rich variety of models that might impact consumption decisions, (ii) to use the simulation as a means to evaluate persuasive technologies in-silico prior to deployment. The model novelty lies in its capacity to connect the determinants of energy related behaviour (values, energy literacy and social practices) and several generic design strategies proposed in the area of persuasive technologies within one framework. We validate the framework using survey data and personal value and energy consumption data extracted from a 2-year field study in Exeter, UK. The preliminary evaluation results demonstrate that the model can predict energy saving behaviour much better than a random model and can correctly estimate the effect of persuasive technologies. The model can be embedded into an adaptive decision-making system for energy behaviour change.

TripEnergy: Estimating Personal Vehicle Energy Consumption Given Limited Travel Survey Data

Estimating personal vehicle energy consumption is important for nationwide climate policy, local and statewide environmental policy, and technology planning. Transportation energy use is complex, depending on vehicle performance and the driving behavior of individuals, as well as on travel patterns of cities and regions. Previous studies combine large samples of travel behavior with fixed estimates of per mile fuel economy or use detailed models of vehicles with limited samples of travel behavior. This paper presents a model for estimating privately operated vehicle energy consumption—TripEnergy—that accurately reconstructs detailed driving behavior across the United States and simulates vehicle performance for different driving conditions. The accuracy of this reconstruction was tested by using out-of-sample predictions, and the vehicle model was tested against microsimulation. TripEnergy consists of a demand model, linking GPS drive cycles to travel survey trips, and a vehicle model, efficiently simulating energy consumption across different types of driving. Because of its ability to link small-scale variation in vehicle technology and driver behavior with large-scale variation in travel patterns, it is expected to be useful for a variety of applications, including technology assessment, cost and energy savings from ecodriving, and the integration of electric vehicle technologies into the grid.

Statements about climate researchers’ carbon footprints affect their credibility and the impact of their advice

Would you follow advice about personal energy conservation from a climate specialist with a large carbon footprint? Many climate researchers report anecdotes in which their sincerity was challenged based on their alleged failure to reduce carbon emissions. Here, we report the results of two large online surveys that measure the perceived credibility of a climate researcher who provides advice on how to reduce energy use (by flying less, conserving home energy, and taking public transportation), as a function of that researcher’s personal carbon footprint description. Across the two studies, we randomly assigned participants to one of 18 vignettes about a climate scientist. We show that alleged large carbon footprints can greatly reduce the researcher’s credibility compared to low footprints. We also show that these differences in perceived credibility strongly affect participants’ reported intentions to change personal energy consumption. These effects are large, both for participants who believe climate change is important and for those who do not. Participants’ politics do affect their attitudes toward researchers, and have an extra effect on reported intentions to use public transportation (but not on intentions to fly less or conserve home energy). Credibility effects are similar for male and female climate scientists.

Energy Consumption Evaluation Based on a Personalized Driver-Vehicle Model

A new approach to evaluate personalized energy consumption is presented in this paper. The method consists of identifying driver–vehicle dynamics using the probability weighted autoregressive model, which is one of the multi-mode ARX models, and then of reproducing the driver–vehicle behavior in a vehicle-following task. The energy consumption of the vehicle is estimated from the velocity profile calculated by using the driver–vehicle model. In this paper, driving simulator and real-world driving data were recorded to identify the driver–vehicle model in various situations. As a result, real-world energy consumption could be reproduced in a variety of situations with an average error of 1.9% and a standard deviation within 1.5%. Several promising applications of the energy consumption evaluation are introduced in this paper, such as an online energy consumption prediction, a powertrain choice-assistance system for car buyers, and a solution to estimate the macroscopic energy consumption of aggregated vehicles in a traffic flow.

Reduction of nutrients, microbes, and personal care products in domestic wastewater by a benchtop electrocoagulation unit.

To preserve environmental and human health, improved treatment processes are needed to reduce nutrients, microbes, and emerging chemical contaminants from domestic wastewater prior to discharge into the environment. Electrocoagulation (EC) treatment is increasingly used to treat industrial wastewater; however, this technology has not yet been thoroughly assessed for its potential to reduce concentrations of nutrients, a variety of microbial surrogates, and personal care products found in domestic wastewater. This investigation's objective was to determine the efficiency of a benchtop EC unit with aluminum sacrificial electrodes to reduce concentrations of the aforementioned biological and chemical pollutants from raw and tertiary-treated domestic wastewater. EC treatment resulted in significant reductions (p < 0.05, α = 0.05) in phosphate, all microbial surrogates, and several personal care products from raw and tertiary-treated domestic wastewater. When wastewater was augmented with microbial surrogates representing bacterial, viral, and protozoan pathogens to measure the extent of reduction, EC treatment resulted in up to 7-log10 reduction of microbial surrogates. Future pilot and full-scale investigations are needed to optimize EC treatment for the following: reducing nitrogen species, personal care products, and energy consumption; elucidating the mechanisms behind microbial reductions; and performing life cycle analyses to determine the appropriateness of implementation.

Connectedness and its consequences: a study of relationships with the natural environment

AbstractRecent interest in people's engagement with the natural environment has resulted in a series of empirical measures and important research findings. Two studies reported here complement this literature in producing a concise measure of connectedness to the natural environment and assessing its independent predictive impact within the structure of the theory of planned behavior. In Study 1 (n = 71), new measures of connectedness and caring were constructed; in Study 2 (n = 163), the connectedness measure was shown to be an independent predictor of people's intentions to reduce personal energy consumption levels. It is suggested that the measure of connectedness may capture some identity‐related and/or affective experience that impacts (independently of more cognitive predictors) upon people's motivation in this domain.

Real-time, appliance-level electricity use feedback system: How to engage users?

Engage is a rapidly deployable, retrofit energy monitoring system developed for direct support of a novel energy use behavior investigation and a large-scale deployment in campus apartments. We describe the end-to-end system and report results related to web dashboard engagement during a year-long experiment. The objective was to determine user engagement with real-time and easily accessible information about personal energy consumption. Leveraging low-cost components, this system was designed to measure separately appliance plug load, heating and cooling, and lighting electrical load in dense-occupancy building environments. We developed and used an open source technology for measurement of plug load and developed signal processing algorithms to significantly improve measurement accuracy. We also developed proxy sensors to measure heating and cooling and lighting. Our results indicate that 90% of the dashboard activity was undertaken by 50% of the participants and that website engagement was more likely in mid-day and more effective in combination with email reminders. Energy conservation was achieved when combining the dashboard with public information about energy consumption.

Modelling teenage personal contexts to support technology enhanced enquiry into personal energy consumption

Reducing energy consumption is becoming an increasingly high government priority. Teenagers are an important category of future energy consumers, but little is known of their conceptions about energy and, particularly, how formal learning about energy translates into understanding of personal energy consumption. We have worked with teenagers to investigate the design of learning technologies that support understanding about energy consumption in a way that is relevant to teenagers' personal contexts, including their motivations, concerns and conceptions, their personal energy consumption, and the sources of information about energy already available to them. In this paper we report on our findings and participatory design methodology. The findings contribute to our understanding of the support learning technologies must provide to scaffold teenagers' learning about personal energy consumption, in terms of how teenagers conceptualise energy consumption, what skills they have in searching for information, and what they consider motivating and relevant. Our methodology, more generally, contributes to our understanding of the initial phases of developing learning technologies that are grounded in an understanding of learners' personal contexts.

Understanding teenagers' personal contexts to design technology that supports learning about energy consumption

Energy sustainability is prevalent in political and popular rhetoric and yet energy consumption is rising. Teenagers are an important category of future energy consumers, but little is known of their conceptions about energy and energy saving. We report on empirical research with two groups of teenagers. This is part of ongoing work to design learning technologies that support teenagers learn about personal energy consumption. In this paper we describe our analysis and methodology, which are shaped by the Ecology of Resources (EoR) design framework [Luckin, R. (2010). Re-designing learning contexts: Technology-rich, learner-centred ecologies. London and New York, NY: Routledge]. Our findings informed the development of an EoR model of the participants' personal context, which includes their world resources (people, tools, knowledge, skills, and environment) and their personal resources (conceptions, motivations and concerns around energy consumption). We discuss the range of methods we employed to understand learners' personal contexts. These findings contribute to our understanding of how to explore teenagers' personal contexts and have implications for the design of technology to support learning about personal energy consumption.

Highly-resolved modeling of personal transportation energy consumption in the United States

This paper centers on the estimation of the total primary energy consumption for personal transportation in the United States, to include gasoline and/or electricity consumption, depending on vehicle type. The bottom-up sector-based estimation method introduced here contributes to a computational tool under development at The Ohio State University for assisting decision making in energy policy, pricing, and investment.In order to simulate highly-resolved consumption profiles three main modeling steps are needed: modeling the behavior of drivers, generating realistic driving profiles, and simulating energy consumption of different kinds of vehicles.The modeling proposed allows for evaluating the impact of plug-in electric vehicles on the electric grid – especially at the distribution level. It can serve as a tool to compare different vehicle types and assist policy-makers in estimating their impact on primary energy consumption and the role transportation can play to reduce oil dependency.