Electricity Consumption Habits Research Articles

Driving factors of residential demand response for the integration of variable renewable power.

The large-scale integration of renewable power poses great challenges to grid stability. Among flexible resources, demand response (DR) stands out for its advantages in cost and efficiency. To identify key factors influencing DR, this study adopted the modified theory of planned behavior (TPB) to establish the conceptual model. Social norms were included as a front-end variable, and institutional factors and electricity consumption habits served as moderating variables. The model was subsequently tested and modified using the structural equation modelling (SEM). Results indicated that social norms can exert a substantial indirect effect on DR behavior. However, due to the deficiency of such norms, the formation of the positive attitude towards DR was hindered, resulting in a low standard coefficient of 0.16. Moreover, the influence of subjective norm on response intention was rejected due to limited perceived external pressure. Perceived behavior control exhibited the most significant direct influence on response intention (0.76). Additionally, the positive effects of situational factors and personal habits on the conversion from response intention to behavior were supported. Based on these findings, several policy suggestions including enhancing publicity and incentive policies were proposed.

Sustainable EnergySense: a predictive machine learning framework for optimizing residential electricity consumption

In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application utilizes Linear Regression as a machine learning (ML) algorithm to develop the electricity consumption forecasting model for the next months, based on past utility bills. Linear regression is often considered one of the most computationally lightweight ML algorithms, making it suitable for smartphones. The application also offers users practical tips for optimizing their electricity consumption habits.

Elicitation of electricity consumption habit formation among new subscribers

In this study, our aim is to explore the formation of electricity consumption habits in Cote d'Ivoire, specifically focusing on new subscribers. The growth of residential electricity consumption globally has been influenced by these habits, which can lead to inertia and inefficient energy usage. Using the unique database of the National Electricity Company, we uncover that electricity consumption habits exhibit a high level of persistence, and this persistence tends to strengthen over time. These findings highlight the need to develop policies that encourage electricity efficiency among new subscribers, to combat energy inefficiency and foster positive consumption habits at the household level.

Residential Electricity Consumption Prediction Method Based on Deep Learning and Federated Learning Under Cloud Edge Collaboration Architecture

Traditional residential electricity prediction methods have problems, such as difficulty in ensuring user privacy and poor convergence speed due to the influence of Different Residential Electricity Consumption (REC) habits. A REC prediction method based on Deep Learning (D-L) and Fed-L under the Cloud Edge Collaboration (CEC) architecture is proposed to address the above issues. First, based on the CEC architecture, combining edge computing and cloud computing center server, the overall model of REC prediction is built. Then, Federated Learning (Fed-L) and D-L model Empirical Mode Decomposition - Long Short-Term Memory (EMD-LSTM) were introduced on the edge side, and the edge side Fed-L depth model was personalized by using EMD-LSTM. Finally, aggregation of edge side models was achieved in the cloud by receiving encrypted model parameters from the edge side and updating and optimizing all edge side models. The results show that the proposed method has the highest REC prediction accuracy, reaching 96.56%, and its performance is superior to the other three comparative algorithms.

A comparative assessment of the power generation via S-shape and M-shape PV system and its impact on a residential consumer

This paper compares the differences in power generation and resident experience between S-shape and M-shape photovoltaic (PV) systems at the same location with the same number of modules. First, the sky anisotropy model (Hay-Davies-Klucher-Reindl, HDKR) is used to simulate solar irradiance (direct, diffuse, and reflection radiation) received by solar modules based on the laws of movement between the Earth and the Sun in the solar system. Next, the annual output power of two different PV system arrangements is calculated based on the solar irradiation received. Furthermore, because the solar module receives three types of solar radiation, interference is added to each of the three parts one by one to simulate shadows cast by moving objects such as birds, leaves, or clouds. Subsequently, off-grid and grid-connected power consumption models are established, and two different photovoltaic power generation systems are used at the power generation end. Lastly, the photovoltaic systems were tested based on residents’ electricity consumption habits, considering both off-grid and grid-connected households. Among them, the grid-connected model adopts the Particle Swarm Optimization (PSO) algorithm to generate an intelligent and flexible power consumption strategy. The results, based on data from Naha, Japan, show that with the same number of modules, the M-shape arrangement generates less annual power than the S-shape. In summer, though, the M-shape can generate more power, and the two structures have similar immunity to disturbances. The S-shape and M-shape have little difference in the power consumption experience. However, the general applicability of these results to other regions remains uncertain, and further testing is needed to account for environmental variations.

Research on fair residential critical peak price: Based on a price penalty mechanism for high-electricity consumers

We construct a fair residential critical-peak price (CPP) optimization model based on a penalty mechanism to address the peaking pressure on the grid and the cost of new energy abandonment caused by the yearly increase in residential electricity load and the significant increase in new energy capacity. According to the electricity consumption habits of different residential consumers, we use the K-means clustering method to identify and classify high-electricity consumers (HECs), low-electricity consumers (LECs), and normal-electricity consumers (NECs) during the critical peak and valley load hours respectively, and design a penalty mechanism of electricity price for HECs (PMEP-H) that penalizes HECs during the critical peak load hours, and compensates the total penalty amount to LECs during the critical peak load hours and HECs during the valley load hours, then calculate the residential electricity load after implementation of PMEP-H based on consumer psychology. The CPP optimization model is constructed by considering the constraints of consumer satisfaction with electricity, tariff penalty, compensation balance, penalty and compensation price, power balance and new energy-generating units output power, and the objective function of high comprehensive system efficiency represented by the difference between peak cost of thermal generation, the benefit of new energy consumption and benefit of the grid side. The model is then solved using an improved particle swarm optimization algorithm for a real residential area in a populated area in northern China. The results show that the developed model can connect the generation, grid and user sides through the electricity consumption tariff, which generates considerable benefits in both peak cutting and new energy consumption targets and can enhance the comprehensive benefits of the generation, grid and user sides system to achieve the supply-grid-load linkage, providing a reference for the improvement of the CPP mechanism in China.

Non-Intrusive Load Monitoring (NILM), Interests and Applications

In developing effective energy management mechanisms, new concepts have been developed to provide new approaches. Non-intrusive load monitoring (NILM) is an approach that was originally developed to allow the occupants of a room to identify the contribution of each appliance to the total electricity consumption of the room through a single point measurement device. The aim is to provide customers with information that will enable them to act as `` ` consum'actors", i.e., people who undertake to change their electricity consumption habits for an objective cause. The progress of artificial intelligence in its various forms (machine learning, big data, internet of things) have greatly contributed to increase the interest of NILM among researchers in different fields. Indeed, some of them are adapting this concept to research areas such as water, transport, health, the environment and agriculture. In this context, applications in these fields have been developed to show the potential and benefits of using this approach. In addition to presenting non-intrusive load monitoring (NILM) in its general framework, this article presents the interests and applications of this approach in various fields.

Hollow village detection method based on non-intrusive power load monitoring

With the development of urbanization, the population and resources converge to the city, leading to different degrees of hollow village phenomenon in rural areas. Therefore, a hollow village detection model based on non-invasive load monitoring method is proposed in this paper. Firstly, the power load data of users is collected in rural areas, and the data is processed and analyzed. A data analysis algorithm based on the improved Attention mechanism is constructed, and the existing public data sets are used for verification and improvement. Six kinds of frequently used electrical appliances among rural users were selected as typical electrical appliances, the power load curves of selected typical electrical appliances were obtained, and the long and short term memory network algorithm was used to identify the types of household electrical appliances of rural users. After algorithm recognition and analysis, the usage characteristics of typical rural electric appliances are obtained, and it is taken as the electricity consumption behavior habits of rural residents. According to their electricity consumption habits, rural residents are divided into four categories: young people living alone, the elderly, two adults and a child, and vacant houses. According to the four groups of people, the index of hollow village is determined. By judging the corresponding household population of four groups of rural residents, the selected village is judged whether it is hollow village or whether there is a tendency of hollow village.

Combined optimal dispatching of wind-light-fire-storage considering electricity price response and uncertainty of wind and photovoltaic power

The high proportion of renewable energy connected to the power grid puts enormous pressure on the power system for peaking. To reduce the peak-to-valley load difference, reduce the abandoned wind and light rate, and improve the economy of power system peaking, this paper constructs a wind–light–fire-storage joint optimal dispatching model based on electricity price response and uncertainty of wind and photovoltaic power. First, the model adds electricity price response to guide customers to change their electricity consumption habits by adjusting electricity prices at different times, bringing economic benefits to customers while shifting part of the peak load to the trough to achieve the purpose of peak shaving and valley filling; Secondly, considering the impact of wind and photovoltaic output uncertainty on power grid peaking, chance-constrained programming is added to the model, which is based on the simulation method, using the Latin hypercube sampling method, eventually transformed into a mixed integer linear programming model for solution; Finally, simulation is carried out in the modified IEEE30 node system to verify the feasibility and effectiveness of the model.

Asynchronous Distributed IoT-Enabled Customer Characterization in Distribution Networks: Theory and Hardware Implementation

This paper proposes and implements an asynchronous distributed IoT-enabled customer characterization framework to classify customers’ load consumption behaviors in electric distribution networks. More specifically, the proposed framework enables robust distributed clustering of customers’ electricity consumption habits in a highly scalable and interoperable framework. The proposed clustering method eliminates the need for hefty synchronization efforts typical to other distributed clustering algorithms. The theoretical foundations of designing the proposed framework are introduced. The orchestration of the application layers integrated in the proposed framework is demonstrated in an experimental implementation on a real-time network adopting a data-centric databus architecture. The results of the experimental implementation on IoT development boards demonstrate that the proposed framework can characterize customer categories with a 95% accuracy compared to classical centralized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering, while ensuring seamless and interoperable peer-to-peer information exchange. This proved to be highly scalable and applicable to the real-world.

High-Dimensional Energy Consumption Anomaly Detection: A Deep Learning-Based Method for Detecting Anomalies

With the increase of energy demand, energy wasteful behavior is inevitable. To reduce energy waste, it is crucial to understand users’ electricity consumption habits and detect abnormal usage behavior in a timely manner. This study proposes a high-dimensional energy consumption anomaly detection method based on deep learning. The method uses high-dimensional energy consumption related data to predict users’ electricity consumption in real time and for anomaly detection. The test results of the method on a publicly available dataset show that it can effectively detect abnormal electricity usage behavior of users. The results show that the method is useful in establishing a real-time anomaly detection system in buildings, helping building managers to identify abnormal electricity usage by users. In addition, users can also use the system to understand their electricity usage and reduce energy waste.

Spatial–temporal short-term load forecasting framework via K-shape time series clustering method and graph convolutional networks

Load forecasting, especially short-term load forecasting, is of great significance to the safe operation of power grids and power system optimization. Historical data is commonly applied in the most of existing methods of load forecasting to predict the future data in one time step, which is considered to have a positive effect on the forecasting performance. However, a serious problem is ignored, which is the assumption that the interactions between end-users are often not considered. Such interaction could be considered as a paired non-Euclidean relationship, which could enhance the accuracy of forecasting effects. In this paper, we propose a graph convolutional network prediction framework based on K-shape time series clustering method for short-term load forecasting. User clusters with similar electricity consumption habits could be separated from a large number of users by the proposed framework. Simultaneously, the paired non-Euclidean relationship between users can also be captured by this framework. The proposed framework is evaluated by the dataset of business users in the southeastern United States. The results show that compared with baselines, the framework significantly improves the prediction accuracy in any scale of user groups, as well as future data in one time step and multi-time steps.

An empirical survey of critical factors influencing students’ electricity use behaviour in halls of residence

PurposeUnderstanding electricity use behaviour is considered one of the strategies to achieve sustained electricity management in buildings. The lack of understanding of occupants’ electricity use behaviour has been found to cause various environmental and ecological issues. This paper aims to investigate the factors influencing occupants’ inefficient use of electricity in buildings becomes a vital area of study to achieve maximum benefit in the area of electricity management.Design/methodology/approachThe study adopted a quantitative survey and questionnaire as instruments for gathering relevant information from end-users in the study area, and the data collected were analysed using descriptive and inferential statistics.FindingsThe major factors influencing the electricity use behaviour of students in the study area were attributed to their level of awareness, personal beliefs and attitude towards electricity, managerial influences and economic factors.Originality/valueThe threats to the environment and ecology necessitate immediate attention to the elements that impact students’ electricity use habits. This research explains the key elements that might impact students’ electricity consumption habits in buildings. Understanding these key characteristics will provide policymakers with vital knowledge of its prevalence.

Non-intrusive load monitoring algorithm based on household electricity use habits

The construction of smart grid is an important part of improving the utilization rate of electric energy. As an important way for the construction of smart grid, non-intrusive load decomposition methods have been extensively studied. In this type of method, limited by transmission cost and network bandwidth, low-frequency data has been widely used in practical applications. However, the accuracy of device identification in this case faces challenges. Due to the relatively single characteristics of low-frequency data, it is difficult to express the operating status of complex electrical appliances, resulting in low decomposition performance. In this paper, a non-intrusive load is proposed based on household electrical habits by studying the relationship between household electricity consumption habits and load status decomposition method. The Gaussian mixture model and time information are used to model the probability distribution of the electrical appliance state. This probability distribution is then used as the observation probability distribution of the factor hidden Markov model. In such a way, the BH-FHMM model is proposed. Finally, load decomposition is carried out through the load decomposition process of the FHMM model. In order to verify the performance of the proposed method, an experimental comparison is conducted based on the REDD data set. According to the results, a significant improvement in equipment recognition accuracy is obtained.

Privacy Protection Method of Electric Power Network Based on Blockchain

In order to ensure the integrity and confidentiality of power data in the era of big data, it is very important to protect the privacy of smart grid data information. Analyzing electricity big data can obtain electricity consumption habits of electricity users to guide the formulation of power supply plans. This experiment uses an asymmetric encryption algorithm. The encryption and decryption of the asymmetric encryption algorithm use two different keys to fully guarantee the security of the blockchain system. The experiment is based on the blockchain technology in photovoltaic microgrid electricity trading, which belongs to the current type of trading, which is divided into five stages. The service provider is a node of the blockchain system. All smart contracts are encrypted by the private key of both parties in the transaction and the public key of the service provider, which fully guarantees the privacy of users. The experimental data shows that the smart contract is decrypted by the private key and the public key of both parties, so as to obtain the relevant information of the transaction and make the corresponding power dispatch work during the transaction period.The blockchain system solves the security and privacy issues of both parties in the absence of a third-party intermediary. Therefore, the use of blockchain technology can achieve a higher degree of automation for point-to-point power transactions.

Classification Analysis Method for Residential Electricity Consumption Behavior Based on Recurrence Plot (RP) and Convolutional Auto-Encoder (CAE)

Load clustering is the foundation of big data mining in power distribution system. It is helpful for power companies to accurately grasp users’ electricity consumption habits, improve power quality and develop demand response. To overcome the characteristic redundancy problem of the high-dimensional load data, the load clustering method based on RP and CAE is proposed. Firstly, the one-dimensional load curves are converted into two-dimensional recurrence plot to realize feature enhancement. Secondly, the advanced feature extraction capability of CAE is used to realize load feature extraction and dimension reduction. Finally, the spectrum clustering (SC) is used to analyze the user’s electricity consumption patterns. The validity of proposed method is verified by Ireland Smart meter dataset.

Optimal dynamic electricity consumption function estimation: an institutional experimental evidence from Guangzhou, China (1949-2016)

This research demonstrates from a dynamic optimal perspective that electricity consumption for a metropolitan area is a function of economic output, electricity consumption habits, and electricity demand management reform. The empirical results include: (1) an unidirectional Granger causality exists linking economic output to electricity consumption; (2) given electricity consumption habits under the context of the electricity demand management reform, an economic output increase of 1% results in the increase of electricity consumption by 0.22%, and (3), after demand management has been implemented, economic output continues to increase electricity consumption, but at a lower rate than prior to reform. These empirical results imply that the ‘conservation hypothesis’ is upheld over the long-run at the regional level in Guangzhou from 1949 to 2016.

Research on Load Forecasting of Charging Station Based on XGBoost and LSTM Model

At this stage, due to the increasing use of electric vehicles, the position of electric vehicle load scheduling in grid power scheduling is becoming more and more important. Effective electric vehicle power dispatching can balance the peak-valley difference of power dispatching, increase the power supply utilization rate of power grid dispatching, and reduce the power supply pressure of line transformer. The load forecast can describe the user’s electricity consumption habits in the next period of time, and can provide important data basis for power dispatching. This paper summarizes the research status of electric vehicle charging load, analyzes traditional charging load research methods, propose a charging load forecasting method combining XGBoost(Extreme Gradient Boosting) and LSTM (Long Short Term Memory Network), And use the data of a charging station in Jiangsu to verify the calculation example. The proposed method is based on the prediction results of the XGBoost model for feature engineering, extracting data features using phase space reconstruction techniques and statistical methods. In addition, training the LSTM model for load prediction. Based on the charging record data of domestic charging stations, this paper applies the artificial intelligence method to the charging load forecast of domestic charging stations for the first time. The charging station load forecasting method studied in this paper can support the regional load forecasting research of electric vehicles with high permeability, and further optimize power dispatching.

Analysis of The Covid-19 Impact on Electricity Consumption and Production

With the year 2020, the world faced a new threat that affects all areas of life, negatively affects production in all areas and paralyzes social life. The measures and restrictions taken by the country's governments to prevent the epidemic from spreading rapidly in the society with the effect of the coronavirus (Covid-19), which first appeared in China and spread all over the world, brought a new lifestyle. Covid-19 has been much the impact on electricity use and electricity production in the period in Turkey as like other countries. There was a sharp decline in commercial and industrial electricity use. The coronavirus effect has also been reflected in the electricity demand and the consumption amount has undergone a great negative change. Due to the enactment of measures against the new type of coronavirus epidemic and the partial or full-time curfews, electricity consumption was moved to homes, supermarkets and hospitals in April 2020 from places where mass consumption is intense, such as industry, workplaces and educational institutions. In this study, Covid-19 period, the first cases were examined electricity production and consumption in Turkey as of the date it is seen throughout, in comparison with electricity consumption data in the same month of the previous years corresponding to this period, the effects on electricity generation and consumption habits of this period were examined.

Optimal design of home energy management strategy based on refined load model

Based on the physical model of load and the control model of expressing user’s subjective use intention, refined modeling methods are proposed for three typical high-power controllable loads on residential side, namely, heating-ventilation and air conditioning (HVAC) system, electric water heater and charging load of electric vehicle (EV) and the refined models are carried out. First of all, a custom family example is constructed, including the user’s electricity habits, structural characteristics of the house and the load, and the simulation is carried out. The refined definition of technical parameters and the influence of typical and atypical electricity consumption process on load electricity consumption effect are analyzed in detail. Then, an optimization model of energy management strategy aiming at optimizing electricity consumption cost is established based on traditional saving families and modern comfortable families. Simulation results show that the user personalized electricity consumption habits and environment have a significant impact on the control strategy of home energy management system (HEMS).