Analysis of Electricity Consumption Pattern Clustering and Electricity Consumption Behavior

Studying user electricity consumption behavior is crucial for understanding their power usage patterns.However,the traditional clustering methods fail to identify emerging types of electricity consumption behavior.To address this issue,this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns.The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment,agricultural drainage irrigation,port shore power,and electric vehicles.Finally,the proposed method is validated through experiments,where the Davies-Bouldin index and profile coefficient are calculated and compared.Experiments showed that the optimal number of clusters is 4.This study demonstrates the potential of using a fuzzy C-means clustering algorithmin identifying emerging types of electricity consumption behavior,which can help power system operators and policymakers to make informed decisions and improve energy efficiency.

Research on Interpolation Method for Missing Electricity Consumption Data

Missing value is one of the main factors that cause dirty data.Without high-quality data,there will be no reliable analysis results and precise decision-making.Therefore,the data warehouse needs to integrate high-quality data consistently.In the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate.For the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity data.First,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data information.All missing data are randomly interpolated within the upper and lower limits.Then,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption data.At last,this process is implemented iteratively until the missing values do not change.Under a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete data.Also,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.

Research on the optimization strategy of customers’electricity consumption based on big data

Current power systems face significant challenges in supporting large-scale access to new energy sources,and the potential of existing flexible resources needs to be fully explored from the power supply,grid,and customer perspectives.This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption.It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort.The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’actual electricity consumption demand.

The transaction behavior of cryptocurrency and electricity consumption

Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production,increasing network hashrates and electricity consumption.Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity.These changes prompt cryptocurrency miners to become new investors,leading to cryptocurrency price increases.The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified.Hence,this research thus utilizes July 312015–July 122019 data from 13 cryptocurrencies to investigate the short-and long-run causal effects between cryptocurrency transaction and electricity consumption.Particularly,we consider structural breaks induced by external shocks through stationary analysis and comovement relationships.Over the examined time period,we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks,with prices trending together with hashrates.Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption.Therefore,owing to the computational power deployed to wherever high profit is found,transactions are vital determinants of electricity consumption.

Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.

Survey of Residential Air Conditioning Electricity Consumption in Shanghai

With proliferation of electric appliances, residential electricity consumption, in particular the air conditioning load becomes more and more important and shares higher and higher percentage in total consumption in large cities like Shanghai. The paper reports in detail the survey on characteristics of residential electric consumption, in particular the air conditioning consumption. To optimize power system operation and expand power market, the paper concludes that power industry must learn to investigate, open up and adapt itself to power market economy.

IMPACT OF CLIMATE ON SUMMER RESIDENTIAL ELECTRICITY CONSUMPTION IN HONG KONG

The influence of climatic variables and cooling degree days (CDD) on summer residential electricity consumption for the period 1980 through 1994 in Hong Kong was investigated. The association between Clo, a measure of amount of Clothing insulation to maintain comfort, and residential electricity consumption was also examined. Utilizing monthly data and multiple regression analyses, it is discovered vapor pressure was not significantly related to electricity consumption while Cloud cover was negatively associated with electricity use. Climatic variables, CDD and Clo provided highly comparable results in modeling summer residential electricity consumption. Mean temperature and Cloud gave the best result. Clo yielded a slightly higher R2 value (0.867) than that of CDD (0.865) in the models. These results indicated that Clo could replace the weather variables and CDD to model electricity consumption.

Power Consumption Characteristics Research on Mobile System of Electrically Driven Large-Load-Ratio Six-Legged Robot

The electrically driven large-load-ratio six-legged robot with engineering capability can be widely used in outdoor and planetary exploration.However,due to the particularity of its parallel structure,the effective utilization rate of energy is not high,which has become an important obstacle to its practical application.To research the power consumption characteristics of robot mobile system is beneficial to speed up it toward practicability.Based on the configuration and walking modes of robot,the mathematical model of the power consumption of mobile system is set up.In view of the tripod gait is often selected for the six-legged robots,the simplified power consumption model of mobile system under the tripod gait is established by means of reducing the dimension of the robot’s statically indeterminate problem and constructing the equal force distribution.Then,the power consumption of robot mobile system is solved under different working conditions.The variable tendencies of the power consumption of robot mobile system are respectively obtained with changes in the rotational angles of hip joint and knee joint,body height,and span.The articulated rotational zones and the ranges of body height and span are determined under the lowest power consumption.According to the walking experiments of prototype,the variable tendencies of the average power consumption of robot mobile system are respectively acquired with changes in duty ratio,body height,and span.Then,the feasibility and correctness of theory analysis are verified in the power consumption of robot mobile system.The proposed analysis method in this paper can provide a reference on the lower power research of the large-load-ratio multi-legged robots.

PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data.The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’needs and their habits,providing better services for users.Nevertheless,users’electricity consumption data is sensitive and private.In order to achieve highly efficient analysis of massive private electricity consumption data without direct access,a blockchain-based federated learning method is proposed for users’electricity consumption forecasting in this paper.Specifically,a blockchain systemis established based on a proof of quality(PoQ)consensus mechanism,and a multilayer hybrid directional long short-term memory(MHD-LSTM)network model is trained for users’electricity consumption forecasting via the federal learning method.In this way,the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data,which is decentralized,secure and reliable.The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation,and can achieve better performance when compared to traditional long short-term memory(LSTM)and bidirectional LSTM(BLSTM).

RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

With the rapid development of the internet of things(IoT),electricity consumption data can be captured and recorded in the IoT cloud center.This provides a credible data source for enterprise credit scoring,which is one of the most vital elements during the financial decision-making process.Accordingly,this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data.Instead of predicting the credit rating,our method can generate an absolute credit score by a novel deep ranking model–ranking extreme gradient boosting net(rankXGB).To boost the performance,the rankXGB model combines several weak ranking models into a strong model.Due to the high computational cost and the vast amounts of data,we design an edge computing framework to reduce the latency of enterprise credit evaluation.Specially,we design a two-stage deep learning task architecture,including a cloud-based weak credit ranking and an edge-based credit score calculation.In the first stage,we send the electricity consumption data of the evaluated enterprise to the computing cloud server,where multiple weak-ranking networks are executed in parallel to produce multiple weak-ranking results.In the second stage,the edge device fuses multiple ranking results generated in the cloud server to produce a more reliable ranking result,which is used to calculate an absolute credit score by score normalization.The experiments demonstrate that our method can achieve accurate enterprise credit evaluation quickly.

Influences of Urban Temperature on the Electricity Consumption of Shanghai

By using data of daily electricity consumption and temperature for the period 2003–2007 in Shanghai, the variation of energy consumption and the correlations between energy consumption and temperature are analyzed. The results indicate that winter and summer are the two peak seasons of energy consumption due to the urban residential heating and cooling demand. The base temperature of electricity and daily temperature is 10℃ in winter and 22℃ in summer respectively. When the outdoor temperature is below 10℃, the heating demand becomes obvious, and with over 22℃ the cooling demand. The spatial distribution of cooling degree-days(CDD) and heating degree-days(HDD) clearly shows urbanization effects. By the influence of urbanization the central city experiences greater CDD in summer and lower HDD in winter. The projected temperature for 2011–2050 implies a significant increase in CDD and a decrease in HDD. This may have implications on the future energy demand if the current energy consumption pattern does not change.

Analysis of users’ electricity consumption behavior based on ensemble clustering

Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling better service to be provided to them.Performing power load profile clustering is the basis for mining the users’electricity consumption behavior.By examining the complexity,randomness,and uncertainty of the users’electricity consumption behavior,this paper proposes an ensemble clustering method to analyze this behavior.First,principle component analysis(PCA)is used to reduce the dimensions of the data.Subsequently,the single clustering method is used,and the majority is selected for integrated clustering.As a result,the users’electricity consumption behavior is classified into different modes,and their characteristics are analyzed in detail.This paper examines the electricity power data of 19 real users in China for simulation purposes.This manuscript provides a thorough analysis along with suggestions for the users’weekly electricity consumption behavior.The results verify the effectiveness of the proposed method.

Inter-Provincial Transaction Model in Two-Level ElectricityMarket Considering Carbon Emission and Consumption Responsibility Weights

In the context of the joint operation of China’s intra-provincial markets and inter-provincial trading,how to meet the load demand and energy consumption using inter-provincial renewable energy trading is a key problem.The combined operation of intra-provincial and inter-provincial markets provides a new way for provincial power companies to optimize and clear the intra-provincial power market,complete the intra-provincial consumption responsibility weight index,and consume renewable energy across provinces and regions.This paper combines power generation and consumption within the province,uses inter-provincial renewable energy trading tomeet the load demand within the province and completes the index of intra-provincial consumption responsibility weights.The intra-provincial market trading and inter-provincial market clearing are respectively taken as the upper and lower levels of the model.Under the two-level electricity market operation framework,the upper-level model aims to minimize the expected total operating cost within the province considering the carbon emission cost and the weight of the consumption responsibility,while the lower-level model aims to minimize the inter-provincial renewable energy purchasing cost.Finally,the influence of inter-provincial transaction mechanism,risk aversion coefficient,voucher price,and responsibility weight on operating cost is analyzed.Simulation is used to verify that the proposed model can meet the requirements of the provincial load power consumption and the consumption responsibility weight index,and promote the consumption of renewable energy.

Energy Consumption Analysis and Optimization of Electric Submersible Pump System

Electric submersible pumps account for a considerable proportion in the development of the Bohai Oilfield. Improving the system efficiency of the electric submersible pump wells, ensuring that the units operate in the high-efficiency zone, is essential. Analysis shows that the efficiency of the electric submersible pump system depends on the wear and tear of each component of the submersible pump equipment, the setting of operational parameters, and more importantly, the production status and daily management level of the oil well. Therefore, improving the structural performance of the submersible pump product, optimizing the parameters setting of the oil well, strengthening daily management, establishing a scientific management system, and improving the production management process and system can effectively improve the production efficiency and economic benefits of the oil well, and further achieve the goal of energy saving and emission reduction. In addition, it is necessary to actively promote the concept and technology of energy saving and emission reduction, encourage oilfield enterprises to explore effective measures to reduce the energy consumption of the electric submersible pump system by strengthening the scientific management system, and achieve a green, low-carbon, and high-quality development of oilfield production to achieve the unity of economic benefits, social benefits, and environmental benefits. This article applies the above measures in the P oilfield to achieve energy optimization of submersible electric pump systems, reducing the daily power consumption of single well submersible electric pump systems by 371 kWh per day, increasing the submersible electric pump's lifespan by 200 days, generating considerable project benefits.

Analysis and Prediction of Regional Electricity Consumption Based on BP Neural Network

Electricity consumption forecasting is one of the most important tasks for power system workers,and plays an important role in regional power systems.Due to the difference in the trend of power load and the past in the new normal,the influencing factors are more diversified,which makes it more difficult to predict the current electricity consumption.In this paper,the grey system theory and BP neural network are combined to predict the annual electricity consumption in Jiangsu.According to the historical data of annual electricity consumption and the six factors affecting electricity consumption,the gray correlation analysis method is used to screen the important factors,and three factors with large correlation degree are selected as the input parameters of BP neural network.The power forecasting model uses nearly 18 years of data to train and validate the model.The results show that the gray correlation analysis and BP neural network method have higher accuracy in power consumption prediction,and the calculation is more convenient than traditional methods.

Electricity consumption propensity of different household members in apartment house

South Korea is an energy-guzzling country.Economic reasons in the country force its households to save more energy.Household energy consumption in South Korea has grown slow compared to other sectors and household energy consumption per capita is lower than the OECD (Organization for Economic Co-operation and Development) average.However,its per capita electricity use soared and expected to keep climbing mainly due to the increasing number of one-person household.To establish effective strategy against a possible electricity shortage,the amount of electricity energy consumption needs to be understood clearly first.We adopted both general survey and detailed survey for people living in apartment housings and collected data on electrical instrument use according to individual schedule.Based on these data,we tried to analyze electricity consumption patterns resulting from energy using activities at home and find out electricity using tendency according to each family member's characteristics in apartment housings.

Impacts of Climate Change on Seasonal Residential Electricity Consumption by 2050 and Potential Adaptation Options in Alexandria Egypt

Climate change and associated more frequent, hot or cold, extreme/weather events, as well as increasing temperature may increase future residential demand for electricity for heating and cooling purposes. The paper in hand intends to assess potential impacts of increasing temperature attributed to climate change on seasonal residential electricity consumption in Alexandria city, Egypt. Additionally, it aims to identify and discuss potential soft and hard adaptation options to such impacts. For this purpose, seasonal changes in electricity consumption were investigated. For this purpose, data on monthly residential electricity consumption, population size and income levels at district level as well as maximum monthly temperature in Alexandria city, Egypt over the period 2007-2016 were collected. This is followed by developing a panel-data model to simulate influence of temperature on residential electricity consumption. It was found that there is a significant growth trend over the study period as well as considerable seasonal variation with summer season experienced significant increase in consumption. It was found that increasing temperature, under RCP 2.6 and RCP 8.5, may contribute to significant increase in residential summer electricity consumption by 2050. Different adaptation options to such an increase in consumption, both soft and hard, have been identified and assessed.

Characteristics of electricity consumption of different industry types considering atmospheric condition

Reducing greenhouse gases (RHG) is going on actively in the international movement. In the field of architecture, RHG is an inevitable work. To establish a plan for RHG, firstly we need to reduce energy consumption. Greenhouse gas generated by energy consumption is the main cause of global warming. For this we should know that how much electricity consumption we use. The research targets of this study are commercial buildings with various businesses. Their electricity consumption was analyzed by business units rather than buildings. Each business was divided into 13 sectors according to industrial classification and electricity consumption was analyzed for each industry. For commercial buildings, the electricity consumption is done by the private sector and construction management is an autonomy system in private instead of an integrated management system. In this study, we classified and analyzed the electricity consumption characteristics according to collected data, analyzed the relationship between the electricity consumption with atmospheric temperature through SPSS, and developed an electricity prediction model.

Research on Electricity Consumption Model of Library Building Based on Data Mining

With the exponential development of Chinese population,the massive energy consumption of buildings has recently become an interest subject.Although much research has been conducted on residential buildings,heating ventilation and air conditioning(HVAC),little research has been conducted on the relationship between student’s behavior,campus buildings,and their subsystems.Using classical seasonal decomposition,hierarchical clustering,and apriori algorithm,this paper aims to provide an empirical model for consumption data in campus library.Smart meter data from a library in Beijing,China,is adopted in this paper.Building electricity consumption patterns are investigated on an hourly/daily/monthly basis.According to the monthly analysis,electricity consumption peaks each year around June and December due to teaching programs,social exams,and outdoor temperatures.Hourly data analysis revealed a relatively stable consumption pattern.It shows three different types of daily load profiles.Daily data analysis demonstrated a high relationship between HVAC consumption and building total consumption,with a lift value of 5.9.Furthermore,links between temperature and subsystems were also discovered.Through a case study of library,this study provides a unique insight into campus electricity use.The results could help to develop operational strategies for campus facilities.

Modelling the Impact and Effects of Climatic Variability on Electricity Energy Consumption in the Yendi Municipality of Ghana

One of the cherished assets of every economy is electricity since it has proven to be the major source of energy for industrialization. Developing economies like Ghana have suffered the downside of poor management of the already inadequate electrical energy at its disposal. This is as a result of limited research into factors that influences electricity energy consumption, most importantly, the effects of climatic variables on electricity energy consumption. This research work explores the causal connection between climatic variables and electricity energy consumption, and develops a Vector Auto Regression (VAR) model to determine the influence of the climatic variables in forecasting electricity energy consumption in Yendi Municipality in the northern region of Ghana. The climatic factors considered in this work are;Rainfall (Rain), maximum temperature (Tmax), Sunshine (Sun), Wind (wind) and Relative Humidity (RH). The Granger causality tests employed in this work revealed that aside from Relative Humidity, the end energy consumption is affected by the other four climatic factors under consideration. The impulse response was used to ascertain the active interaction among electricity energy consumption and the climatic variables. The impulse response of electricity energy consumption to the climatic variables indicates a maximum positive effect of Temperature and Sunshine on electricity energy consumption in March and September respectively. The VAR model was also used in forecasting future consumption of electricity energy. The results indicate excellent forecasts of electricity energy consumption for the first four months of 2019.