Energy Consumption Behavior Research Articles

A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types

Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems.

A Two-Stage Framework for Exploiting the Multi-Elasticity of Multi-Energy Demands in Integrated Electricity and Gas Markets

With the development of integrated electricity and gas markets (IEGM), the participation of multi-energy demands (MEDs) is an effective measure to achieve a win-win goal of market efficiency improvement and demand cost reduction. The MEDs, composed of terminal users and energy conversion equipment (ECE), can adjust purchased electricity and gas with the variation in energy prices. Such adjustment behaviors of purchased energy in response to energy prices are defined as the multi-elasticity of MEDs. Considering that, this paper aims to analyze and quantify the multi-elasticity of MEDs and exploit it using a market-based approach. Firstly, the volume-price bidding curves are proposed to describe the multi-elasticity of MEDs considering the price-response features of terminal loads and multi-energy substitution features of ECE. Based on the proposed bidding curves, the energy consumption behaviors of MEDs with the change in prices can be effectively quantified. Moreover, a decentralized two-stage market framework is proposed to exploit the multi-elasticity in the IEGM clearing based on the multi-elasticity bidding curves of MEDs. In the proposed framework, the electricity market and gas market are cleared separately with the information exchange of the MEDs' clearing results of purchased gas and electricity. Furthermore, the consensus ADMM-based algorithm is applied to coordinately solve the IEGM clearing problem integrated with the multi-elasticity of MEDs. Numerical simulations are carried out for different test systems to validate the effectiveness of the proposed models.

Low-carbon economic operation of energy hub integrated with linearization model and nodal energy-carbon price

Energy hub has positive significance in absorbing renewable energy, improving energy utilization efficiency and reducing carbon emissions. Considering both linearization energy hub model and nodal integrated energy-carbon price, a low-carbon economic operation model of energy hub is constructed by taking advantage of the bi-level optimization structure. First, in order to simplify the modeling procedures and improve the solving efficiency, reversibility judgment criteria and linearization processes in energy hub modeling are improved. Then, a nodal integrated energy-carbon pricing strategy is proposed and calculated according to flow tracing method and carbon emission flow method. Additionally, the bi-level optimization model and solution procedure are presented to obtain the optimal operating states of energy hubs. At last, for the simulation results, the characteristics and the influence of nodal price on energy consumption behaviors of energy hubs are analyzed, the proposed model is effective to reduce the carbon emissions and operating costs.

Impact of knowledge and trust on households' solar energy consumption behavior: Do social influence and gender matter?

Clean and renewable energy resources like solar energy are essential for a sustainable and eco-friendly future, as they decrease reliance on fossil fuels and combat climate change. This study explores trust and knowledge as drivers of solar energy consumption behavior (SECB). Particularly, this study finds the role of social influence and gender as moderators in the associations between trust and SECB, as well as knowledge and SECB. The World Bank energy access data based on the 8500 Pakistani households survey was examined for empirical analysis. Findings reveal that trust and knowledge positively impact SECB. Knowledge also influences trust in solar energy. Meanwhile, higher social influence weakens the association of knowledge and trust with SECB. Furthermore, it was found that the association of knowledge and trust with SECB is stronger for females than males. The findings-based implications are provided for practitioners and researchers.

Sustainable Smart City Technologies and Their Impact on Users’ Energy Consumption Behaviour

Sustainable smart cities (SSCs) target decarbonisation by optimising energy consumption through the emerging capabilities of technology. Nevertheless, the energy consumption behaviour of end users has the potential to compromise the effectiveness of technological interventions, reflecting the importance of active social engagement in realising decarbonisation goals. Although extensive research exists on energy consumption behaviour, little is known about how technology engagement affects it, the nature of these technologies, and their role in SSC. The paper aims to identify, categorise, and investigate the smart technologies that impact household energy consumption behaviours and their integration into the larger SSC system. Following a systematic review of 60 articles from the Scopus database (2013–2023), the study found 45 smart technologies cited, with 49% affecting efficiency behaviour and 51% affecting curtailment behaviour. While these technologies inform the city administration level in the SSC framework, the role of end users remains unclear, suggesting a technocratic approach. The study proposes the Sustainable Smart City Network to facilitate a grassroots approach, identifying five key domains: government policies, smart technology adoption, smart technology engagement, smart city infrastructure, and urban sustainability. The study provides an original contribution to knowledge by unveiling the key technologies affecting energy consumption behaviour and outlining the pragmatic requirements for achieving decarbonisation through a grassroots approach.

Electricity–gas multi-agent planning method considering users’ comprehensive energy consumption behavior

With the advent of the energy Internet and the swift growth of unified energy systems, the comprehensive energy demand of users has gradually become a problem that cannot be ignored for the planning of integrated energy systems. Aiming at this problem, this paper suggests a multi-agent planning approach for electricity and gas, considering users’ holistic energy consumption behavior. First, utilizing a combined subjective and objective weighting method, this study establishes a utility model for users’ energy consumption characteristics. The analysis of comprehensive energy consumption behavior is conducted through an evolutionary game. On this basis, the planning revenue model for electricity grid and gas network investors is formulated, and the game mechanism of different investors is analyzed. A dynamic game model of electricity–gas multi-agent planning considering comprehensive energy consumption behavior is proposed. Ultimately, the model is resolved using an iterative exploration approach. The validity and efficacy of the proposed method are confirmed through a simulation example.

Reducing energy poverty by nearly universal pension coverage of rural China

This paper estimates the causal effect of income change on reducing energy poverty by exploiting age-based eligibility for China’s New Rural Pension Scheme (NRPS) through a regression discontinuity (RD) design. More than one billion people worldwide live in energy poverty, and the situation is even more difficult in developing world. It is an important development challenge to reduce energy poverty, which could be further associated with health outcome, labor productivity, and educational achievements. While public policies that increase income for poor households are the most direct ways to reduce energy poverty, the reverse causality makes establishing a convincing causal link between income and energy poverty challenging. Using the quasi-experimental variation in income induced by NRPS, this paper addresses the endogeneity of income and examines the impact of exogenous sharp changes in income on energy consumption behaviors. The findings indicate substantial increases in modern energy consumption and sizable reductions in solid fuels (e.g., straw and fuelwood), suggesting that “windfall income” by pension coverage alleviates energy poverty. Although individuals could anticipate the income shocks by pension coverage, the evidence suggests that they do not engage in anticipatory responses to smooth their consumption. Further evidence shows that liquidity constraints may be the underlying mechanism for the lack of anticipatory responses in household energy consumption. Besides reducing energy poverty, the results demonstrate that pension coverage leads to a decrease in elderly labor supply, an increase in subjective health and life satisfaction, and an increase in non-energy consumption, all of which are positively associated with the well-beings of senior citizens. We anticipate that the findings of this paper in the context of China may be extended to developing world which has been expected to set up targeted measures to tackle energy poverty in upcoming decades.

A policy design perspective on electricity rates

Electricity rates affect citizens’ energy-related behaviors. In the past and present, studies of the behavioral influences of the designs of these rates have heavily focused on the structure and effects of pricing mechanisms. This price-centric perspective has, this paper demonstrates, led other crucial aspects of rate designs to be overlooked despite their potential implication for more efficient and equitable citizen energy access and climate change mitigation. To explore the composition of rate designs beyond the oft-examined pricing component, this study develops and utilizes a novel application of policy design scholarship to more comprehensively examine the rate designs that facilitate residential electricity provision and shape energy consumption behaviors. Through this approach, multiple design elements and more granular sub-elements are identified in contemporary rate designs. Furthermore, emergent properties of rate design diversity and complexity are discerned as design features occur individually and combinatorically. Ultimately, this study not only tractably expands the concept of rate designs, but also advances a method for examining the policy instruments through which public service provision occurs.

Household Sustainability During COVID-19: Investigating Shifts in Food, Water and Energy Consumption Behaviours in Bayelsa State, Nigeria

This comprehensive study investigates the impact of the COVID-19 pandemic on pro-environmental behaviours within Nigerian households. It extensively reviews literature exploring various models and theories, including Chernovich’s model and the Protection Motivation Theory (PMT), to understand the intricate factors influencing household eco-friendly conduct. Employing a correlational study design, the research utilized both probability and non probability sampling techniques to gather data through self-administered questionnaires from households in Yenagoa city, Nigeria. Findings revealed a significant increase in pro-environmental behaviors during the pandemic-induced lock down. Statistical analysis highlighted a notable rise in actions such as conserving energy, minimizing water usage, and reducing food waste. The study observed heightened household efforts in shutting off electrical appliances, adopting water-saving measures, and practicing better food management during the lock down period. Analysis of Household Conservative Behaviors (HCB) before and during the lock down showed a marked increase in eco-friendly actions, signifying the pandemic’s positive influence on environmental consciousness. Recommendations stemming from this research include advocating for awareness campaigns, financial incentives, community engagement, educational integration, policy advocacy, and continuous research to sustain and enhance pro-environmental behaviors. The study underscores the importance of crisis-preparedness strategies integrating sustainability considerations and tailoring interventions to diverse cultural contexts. These efforts aim to foster a culture of sustainability, promote awareness, and encourage proactive engagement in eco-friendly practices among Nigerian households, particularly during crises like the COVID-19 pandemic and in everyday life.

Analysis of the thermal behavior of a fixed bed reactor during the pyrolysis process

Pyrolysis is a thermochemical process of degradation of organic compounds where the reaction takes place in an inert atmosphere. The process scale varies between industrial, semi-industrial or laboratory. During the pyrolysis process temperature has to be controlled, but, most of pyrolysis studies do not clearly state where the temperature is measured and weather the temperature field is uniform. In this paper thermal behavior of a laboratory scale fixed-bed reactor and energy consumption during pyrolysis processes were analyzed. Three different samples were used: mixture of plastic waste (sample 1), biomass (sample 2) and mixture of plastic waste and biomass (sample 3). The analysis of the thermal behavior of the reactor indicates that with careful regulation or temperature control of the process, one can obtain diagrams that can be used for the purpose of recording thermally intensive processes, similar to more complex thermogravimetric (TG) and derivative thermogravimetric (DTG) analyses. It has been shown that it is possible to change the heating rate and the overall energy efficiency of the process by simply choosing the appropriate raw material mixture.

Energy Intelligent Control and Energy Saving System for Computer Room Based on Artificial Intelligence

In order to understand the energy intelligent control energy-saving system in computer rooms, the author proposes a research on an artificial intelligence based energy intelligent control energy-saving system in computer rooms. The author first analyzed the energy monitoring and analysis system of artificial intelligence in computer rooms. Based on the existing energy consumption data resources of computer rooms, through monitoring and scientific analysis of various aspects of energy consumption data in computer rooms, it provides decision-making support for managers to better formulate energy-saving measures. The system functions are divided into four sub modules: energy consumption environment monitoring, energy consumption behavior analysis, energy-saving renovation suggestions, and energy-saving effect evaluation; We designed an energy consumption anomaly detection method using the GESD algorithm and implemented a B/S architecture using the SSH framework. Finally, in terms of data display, we utilized two technologies: Extjs and BIRT reports, and used POI to export energy consumption data files in Excel format. Practical applications have shown that the system provides assistance for managers to develop energy-saving and emission reduction measures.

A real time condition based sustainable maintenance method for milling process

The manufacturing industry must reduce energy consumption during the machining process to comply with energy demand and usage restrictions. Previous research has employed various methods to take a optimization plan in machining process. However, these studies mostly rely on static machining conditions, without paying much attention for the condition variation, which may lead to a coarse energy efficiency optimization in machining process. Hence, this study introduces a real time condition based sustainable maintenance method for machining process. Firstly, a real-time data acquisition system is employed to collect and send data to the developed tool condition prediction model using deep learning. Then, the physical energy prediction model is employed to reflect the real time energy consumption behaviors based on the real time tool condition. Subsequently, a similarity analysis is employed to decide whether a new optimization is required. And, once it is required, an optimization using NSGA-II and TOPSIS is conducted. Experiments were conducted on a milling machine and compared with traditional methods, and verified using triple bottom line. Results indicated that the proposed method can reduce the energy consumption by 2.51% and 7.01%, and it can maintain a qualified product during the machining process.

Community approaches to socio-economic aspects of urban energy transition

The increasing proliferation of energy-intensive devices in urban smart homes presents a pressing challenge to the stability of electrical systems, manifesting as the potential for peak load issues, unexpected disruptions, and a consequential decline in economic returns. This research is dedicated to the meticulous optimization of energy consumption, achieved through the implementation of intelligent scheduling protocols for household devices, all while factoring in their distinct characteristics and user preferences. The primary objective of this paper is to introduce a holistic engineering framework tailored to analyze the intricate dynamics of household energy consumption behaviors, with a particular emphasis on the community-level context prevalent in smart urban environments. In addressing the inherent intricacy and nonlinearity inherent in multi-objective challenges within this domain, our proposal revolves around the deployment of a specialized whale optimization algorithm. This algorithm offers a powerful tool to efficiently navigate the complexities of optimization problems, yielding solutions that approach near-optimality. Empirical findings stemming from our experimentation robustly affirm the efficacy of our proposed algorithm. They indicate substantial reductions in energy expenditure, heightened levels of customer satisfaction, and a more equitably balanced load distribution throughout the primary grid. These outcomes, in turn, contribute significantly to the overarching enhancement of global power system performance, all the while aligning harmoniously with the socio-economic considerations central to urban energy transition endeavors.

A component-based design approach for energy flexibility in cyber-physical manufacturing systems

AbstractEnergy flexibility of manufacturing systems helps to meet sustainable manufacturing requirements and is getting significant importance with rising energy prices and noticeable climate changes. Considering the need to proactively enable energy flexibility in modern manufacturing systems, this work presents a component-based design approach that aims to embed energy flexibility in the design of cyber-physical production systems. To this end, energy management using Industry 4.0 technologies (e.g., Internet of Things and Cyber-physical Systems) is compared to the literature on energy flexibility in order to evaluate to what extent the energy flexibility practice takes advantage of Industry 4.0 technologies. Another dimension is the coverage of the life cycle of the manufacturing system which guarantees its sustainable design and the ability to achieve energy flexibility by configuring the energy consumption behaviour. A data-based design approach of the energy-flexible components is proposed in the spirit of the Reference Architectural Model Industrie 4.0 (RAMI 4.0), and then it is exemplified using an electric drive (as a component) in order to show the practical applicability of the approach. The energy consumption behaviour of the component is modelled using machine learning techniques. The digital twin of the studied component is developed using Visual Components virtual engineering environment, then its energy consumption behaviour is included in the model allowing the system integrator/decision-maker to configure the component based on the energy availability/price. Finally, external services in terms of an optimisation module and a deep learning module are connected to the digital twin.

Moral licensing and habits: Do solar households make negligent choices?

Orienting household behaviors towards more efficient use of energy in the home is crucial to climate change mitigation efforts. The objective of this work is to examine the social-psychological antecedents of energy curtailment in solar households. Using an online survey (n = 257) of solar households in Australia, the study validates an augmented model of planned behavior using partial least squares structural equation modeling (PLS-SEM). The study shows that the path of a standard construct, from perceived behavioral control to intentions, is interrupted by moral licensing, a concept that has been largely ignored in studies applying the standard model of planned behavior. The study provides fresh insight into how consumers vindicate wasteful energy consumption behavior through moral licensing. Amongst its contributions, the study shows that social influence is the most important driver of intentions and behavior. The habitualised use of energy has a significant, negative effect on behavior. This work has several recommendations for retailers and policy makers in global energy markets on how to minimise licensing and negligent habits, and at the same time, it opens up new research avenues in the ethical consumption discourse.

Two-level collaborative demand-side management for regional distributed energy system considering carbon emission quotas

In demand response programs, users typically modify their energy consumption behavior in response to the grid. Distributed energy systems (DES) also need users' participation to ensure the efficient and stable operation of the system, especially under government-imposed carbon emission quotas. This study proposes a two-level collaborative demand-side management framework that allows users to participate in demand response in distributed energy systems, thereby ensuring that the systems' carbon emission remain within the quota while minimizing the impact on thermal comfort. Firstly, the framework realizes coordination between the supply and demand side. Changing energy use behavior not only reduces the amount of load but also enhances the system's efficiency. Secondly, different type of users can cooperatively adjust their air conditioners according to their load characteristics to achieve optimal overall thermal comfort. The effectiveness of the proposed method is demonstrated in a DES with four types of buildings, evaluated using two indoor thermal environment indexes. Under optimal guidance, the system efficiency is improved by 1.9%. Additionally, there is a 3.3% reduction in carbon emissions, along with a 13.2% improvement in overall indoor thermal comfort compared with unified temperature regulation. This study holds positive implications for users' participation in distributed energy systems to achieve energy saving and carbon reduction.

PerfECT Design Tool: Electric Vehicle Modelling and Experimental Validation

This article addresses a common issue in the design of battery electric vehicles (BEVs) by introducing a comprehensive methodology for the modeling and simulation of BEVs, referred to as the “PerfECT Design Tool”. The primary objective of this study is to provide engineers and researchers with a robust and streamlined approach for the early stages of electric vehicle (EV) design, offering valuable insights into the performance, energy consumption, current flow, and thermal behavior of these advanced automotive systems. Recognizing the complex nature of contemporary EVs, the study highlights the need for efficient design tools that facilitate decision-making during the conceptual phases of development. The PerfECT Design Tool is presented as a multi-level framework, divided into four logically sequential modules: Performance, Energy, Currents, and Temperature. These modules are underpinned by sound theoretical foundations and are implemented using a combination of MATLAB/Simulink and the vehicle dynamics software VI-CRT. The research culminates in the validation of the model through a series of experimental maneuvers conducted with a Tesla Model 3, establishing its accuracy in representing the mechanical, electrical, and thermal behavior of BEVs. The study’s main findings underscore the viability of the design tool as an asset in the initial phases of BEV design. Beyond its primary application, the tool holds promise for broader utilization, including the development of active control systems, advanced driver assistance systems (ADAS), and solutions for autonomous driving within the domain of electric vehicles.

The effect of the COVID-19 pandemic on Malaysian residential customers’ energy-saving appliance purchasing behaviour

Purpose The COVID-19 pandemic has caused a dramatic impact on energy supply and demand. It is vital to understand households’ behaviour with regard to energy, particularly during the pandemic, to deploy future sustainable energy systems. This study aims to investigate the nexus of Malaysian households’ energy consumption behaviour in relation to various electrical appliances, their energy-saving appliance purchasing behaviour and their current possession of energy-saving appliances during the pandemic, especially during the lockdown period, from the perspective of the energy cultures framework. Design/methodology/approach The partial least squares structural equation modelling technique was used to test hypothesised relationships based on the 1,485 pieces of household data collected using an online and physical survey during the lockdown period in Malaysia. Findings The energy-saving behaviour cultivated due to the impact of the COVID-19 pandemic led to residential customers’ intentions to purchase energy-saving appliances which subsequently led to their current possession of energy-saving appliances. Indeed, energy-saving behaviours in the kitchen, entertainment, office, home lighting and cooling appliances have more than 77.4% influence on their purchasing behaviour. The consumer’s purchase behaviour for energy-saving appliances has a significant, partially mediating influence on the energy-saving behaviour of various electrical appliances and the consumers’ current possession of energy-saving appliances. Research limitations/implications This study could be enhanced by improving the sample using a higher-income group and involving other parts of Malaysia such as the southern region. The findings do extend the energy cultures framework by demonstrating the mediating role of households’ energy-saving appliance purchasing behaviour on the relationship between their energy consumption behaviour in relation to various electrical appliances and their current possession of energy-saving appliances. Practical implications The results of this study will help develop future action plans for transitioning to energy-saving appliance practices. Originality/value This paper examines the effects of the COVID-19 pandemic on future energy efficiency practices in developing countries from the perspective of the energy cultures framework.

An analytical model for enterprise energy behaviors considering carbon trading based on evolutionary game

Carbon trading market is a central policy tool for promoting cleaner enterprises' energy consumption and investment behaviors. It is crucial for enterprises to establish a green and low-carbon development model by clarifying carbon trading impact mechanism on enterprise behaviors and the interactive relationship between entities in the carbon market. China's contract-based carbon market is still in the development stage, the impact of the mainstream allowance mechanism and government regulation on enterprises' energy behaviors has yet to be determined. In view of this, this paper describes mathematical mechanisms of the influence of carbon prices and carbon quotas on enterprises' energy consumption and investment, and the evolutionary game model of government-enterprise behaviors is constructed, the evolution process and appropriate strategies of enterprises' energy behaviors in the development of the carbon market are discussed. This paper uses real data of China carbon market to simulate the model, it is found that strategies such as relaxation of carbon quotas and carbon subsidies dynamization are needed during the initial development of the market. In the mature stage of the carbon market, strategies are needed to increase the proportion of government carbon revenue and reduce its regulatory cost. The higher the initial willingness of government-enterprise, the better the effect of strategies and the constraints of public willingness. The model and results have theoretical implications for enterprises' energy transformation and government regulation, and provide a reference for the development of China's carbon market.

Nonlinear pricing in multidimensional context: An empirical analysis of energy consumption

Modern business practices frequently employ a blend of pricing strategies to segment markets effectively. As a result, consumers may encounter pricing schedules that are nonlinear and multidimensional. This paper presents a structural approach for estimating multidimensional nonlinear pricing models involving multiple decision variables in an energy market. Using a unique, rich panel dataset of Chinese household electricity consumption, we structurally estimate consumer preferences under the influence of an Increasing Block Price (IBP) and a Time-of-Use (ToU) system. Our structural approach allows us to distinguish and evaluate household-level price elasticities of demand, presenting a novel explanation for consumers' feedback on marginal price changes. Through model-based simulations, we demonstrate that a 1% increase in price corresponds to a 0.7% reduction in total electricity demand. However, our analysis indicates that practical opportunities for optimization within multi-dimensional pricing systems are limited. Our findings offer distinct insights into the complex interplay between intricate pricing structures and energy consumption behavior, thereby providing valuable guidance for policymakers and regulators.