Multi-Step Clustering of Smart Meters Time Series:Application to Demand Flexibility Characterization of SME Customers

Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.

Analysis on Influential Factors of Peasants' Willingness to Exit Homestead in Regions with Different Economy Development Levels——Taihe Village,Chaohu County-Level City,and Ancang Village,Taihu County as Examples

The research concluded peasants＇ willingness on homestead exiting and influential factors by questionnaire survey and interviewing in two villages with varied levels of economic development in Anhui Province.Comparisons were performed on subjective factors and objective factors were explored by Logistic regression.The results showed that both of education background and homestead use have positive correlation with peasants＇ willingness on homestead exit;the number of family members has negative effects on peasants in advanced village but the number of migrant workers or members living in cities has positive effects;the area of homestead has positive effects on peasants in underdeveloped village.Therefore,the emphasis should be varied on homestead exit upon village economy and it is necessary to reinforce homestead exit promotion at improving immigrants＇ reception work,social security,employment,and education.

A Prediction Model of Peasants’ Income in China Based on BP Neural Network

According to the related data affecting the peasants’ income in China in the years 1978-2008,a total of 13 indices are selected,such as agricultural population,output value of primary industry,and rural employees.Based on the standardized method and BP neural network method,the peasants’ income and the artificial neural network model are established and analyzed.Results show that the simulation value agrees well with the real value;the neural network model with improved BP algorithm has high prediction accuracy,rapid convergence rate and good generalization ability.Finally,suggestions are put forward to increase the peasants’ income,such as promoting the process of urbanization,developing small and medium-sized enterprises in rural areas,encouraging intensive operation,and strengthening the rural infrastructure and agricultural science and technology input.

Impact of organophosphorous pesticide exposure on semen quality of Chinese peasants

Aim: To investigate the impact of organophosphorous pesticide exposure and other risk factors on the semen quality of Chinese peasants. Method: Questionnaire investigation, external genital examination and laboratory examination were conducted on 322 male peasants, including the exposed and the control groups of 161 subjects each. Results: Multivariate analysis showed that the educational level and the years of alcohol drinking influenced the semen volume. Compared with subjects of primary school education or lower, those with junior high school education had a higher semen volume (OR=1.961). Proportion of subjects with higher semen volume decreased with increased years of alcohol drinking (OR=0.962). Organophosphorous pesticide exposure decreased the number of forward progression sperm (OR=0.528), the sperm density (OR=0.266), the sperm viability (OR=0.398) and the percentage of normal morphology sperm (OR=0.281). A possible relationship was present between the season of semen collection and the number of forward progression sperm, sperm viability and percentage of normal morphology sperm. Compared with the summer data, the above three indices were higher in winter (OR being 2. 272, 4.060 and 5.249, respectively). Conclusion: Exposure to organophosphorous pesticide could damage the semen quality of the peasants. Education and alcohol drinking also affect the semen quality.

Strategies for Cultivating New Peasants Based on Modern Agricultural Development

Development of modern agriculture has higher and higher requirements on quality of peasants.This study,on the basis of connotations of modern agriculture,current situation and problems of Chinese peasants,and contradictions between modern agricultural development and peasants' quality,specifies requirements of modern agriculture on new peasants,proposes corresponding suggestions to promote the cultivation of new Chinese peasants.

Different Characteristics of Peasant Issues from the Connections between Peasants and Land

The paper analyzes the different characteristics of peasant issues formed by connections between peasants and land at different historical stages,and offers a reference for us to look squarely at peasant issues in the reality.The characteristics of peasant issues with land as the inherent feature are:the degree of connection decides the classification and policies of peasants;the private ownership of land decides the political status and influences of peasants;the inherence of private connections with land determines the future of peasants.The characteristics of peasant issues with land as the core value are:the peasant issues with land as the core is the key to victory of new-democratic revolution;the peasant issues with land as the core is the basis of establishment of socialist system;the characteristics of peasant issues with land as the guarantee are:carriers of peasants interests change from unicity to diversity;peasants change from identity certification to vocational certification;the economic influence of peasants changes and penetrates from part to entirety.

Reflections on the Peasants＇ Subjectivity in the Development of Rural Deliberative Democracy

The subjectivity of China's people deliberative democracy is essentially different from Western capital deliberative democracy. And the people's subjectivity naturally manifests as peasant's subjectivity in the deliberative democracy in rural areas. Combined with normative analysis of the whole process of rural deliberation practice,peasants' subjectivity can be summarized as broad equality of participation,completeness of the program,as well as final benefit of the result. However,there are some hidden dangers which may give rise to alienation development of deliberative politics in the current dilemma of rural deliberation practice.The fundamental way to solve the problem lies in combining subjectivity and institutionalization together to develop the rural deliberative democracy.

Approaches of Realizing Cultural Value of Red Revolutionary Site in Jiangxi Province:A Case Study of Chinese Workers' and Peasants' Red Army in Xiushui County

Red revolutionary site is a significant carrier for inheriting and spreading red culture,a witness of revolutionary history and stones,and also the best place to show connotations and value of red culture.Taking the regiment headquarter and the division headquarter of the First Division of the First Army in Xiushui County,Jiangxi Province for example,this paper explored cultural value of red site,analyzed the existing problems,tried to figure out scientific approaches of realizing cultural value of red site,so as to promote the sharing and inheritance of cultural value of red revolutionary site.

Foreign Ducks Help Peasants to Shake Off Poverty

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Differentiation of Peasants and Its Impact on Farmland Function

The implementation of the field responsibility system liberated peasants from the land and started the differentiation of peasants＇ occupation. With the differentiation of peasants＇ occupation and income differentiation, the functions of farmland also change accordingly. Based on the analysis of statistical data. the study found that over the past 30 years, the degree of occupational differentiation of peasants has become more and more complete, and income differentiation has also expanded. At the same time, the security function of traditional farmland is declining, but peasants pay more and more attention to the property function of farmland.

Empirical Analysis on the Peasants＇ Cultivated Land Abandonment Decision-making Behaviors in Central China and the Subjective Factors of the Peasants --Jiangxi Province Is Taken as an Example

Following China＇s rapid advance the process of industrialization and urbanization, a large number of rural labor into cities, abandoned land becomes a universal phenomenon and shows an enlarge spread trend. Therefore, the food security of our country is threatened. This article will according to the farmers＇ angle of view, from the farmers owned their own labor, land and capital three aspects, uses field survey data to analyze the relationship between the situation of human capital, employment policy, farmland status and management decision, income and investment decision-making this five aspects and land reclamation. The result shows that age, education, arable area, whether the land transfer, engaged in non-agricultural sector employment time, work area, per capita income, non-agricultural income and investment has a significant impact on arable land abandonment, and reflect the development potential of the family of peasants and a tendency of development having a positive correlation with arable land abandonment, this phenomenon reflects in our nation, the rural residents are divorced from the original rural living environment and integrate into cities, a gradual process of get rid of the original way of life in the new way.

Foreign Friends in China Two American "Peasants" in China

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Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.

Stochastic programming based coordinated expansion planning of generation,transmission,demand side resources,and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.

Demand-Responsive Transportation Vehicle Routing Optimization Based on Two-Stage Method

Demand-responsive transportation(DRT)is a flexible passenger service designed to enhance road efficiency,reduce peak-hour traffic,and boost passenger satisfaction.However,existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs.Consequently,there is a need to develop realtime DRT route optimization methods that integrate both initial and real-time requests.This paper presents a twostage,multi-objective optimization model for DRT vehicle scheduling.The first stage involves an initial scheduling model aimed at minimizing vehicle configuration,and operational,and CO_(2)emission costs while ensuring passenger satisfaction.The second stage develops a real-time scheduling model to minimize additional operational costs,penalties for time window violations,and costs due to rejected passengers,thereby addressing real-time demands.Additionally,an enhanced genetic algorithm based on Non-dominated Sorting Genetic Algorithm-II(NSGA-II)is designed,incorporating multiple crossover points to accelerate convergence and improve solution efficiency.The proposed scheduling model is validated using a real network in Shanghai.Results indicate that realtime scheduling can serve more passengers,and improve vehicle utilization and occupancy rates,with only a minor increase in total operational costs.Compared to the traditional NSGA-II algorithm,the improved version enhances convergence speed by 31.7%and solution speed by 4.8%.The proposed model and algorithm offer both theoretical and practical guidance for real-world DRT scheduling.

Physics Guided Deep Learning-Based Model for Short-Term Origin–Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic

Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,including real-time availability,sparsity,and high-dimensionality issues,and the impact of the pandemic.Consequently,this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network(PAG-STAN)for metro OD demand prediction under pandemic conditions.Specifically,PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices.Subsequently,a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices.Thereafter,PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic.Finally,a masked physics-guided loss function(MPG-loss function)incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability.PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios,highlighting its robustness and sensitivity for metro OD demand prediction.A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.

Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.

A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.