Meter Data Collection Building Area Network(MDCBAN) deployed in high rises is playing an increasingly important role in wireless multi-hop smart grid meter data collection. Recently, increasingly numerous application ...Meter Data Collection Building Area Network(MDCBAN) deployed in high rises is playing an increasingly important role in wireless multi-hop smart grid meter data collection. Recently, increasingly numerous application layer data traffic makes MDCBAN be facing serious communication pressure. In addition, large density of meter data collection devices scattered in the limited geographical space of high rises results in obvious communication interference. To solve these problems, a traffic scheduling mechanism based on interference avoidance for meter data collection in MDCBAN is proposed. Firstly, the characteristics of network topology are analyzed and the corresponding traffic distribution model is proposed. Next, a wireless multi-channel selection scheme for different Floor Gateways and a single-channel time unit assignment scheme for data collection devices in the same Floor Network are proposed to avoid interference. At last, a data balanced traffic scheduling algorithm is proposed. Simulation results show that balanced traffic distribution and highly efficient and reliable data transmission can be achieved on the basis of effective interference avoidance between data collection devices.展开更多
The choice of meter data acquisition methods has important significance for the electric energy management. Based on the comprehensive analysis of several meter data acquisition methods, this paper assess the performa...The choice of meter data acquisition methods has important significance for the electric energy management. Based on the comprehensive analysis of several meter data acquisition methods, this paper assess the performance of each one by analytic hierarchy process. We can draw a conclusion by calculating＂ The local automatic meter reading, the prepaid electric energy metering and the remote automatic meter reading have almost the same performance. They are better than the manual meter reading and the vehicle mounted mobile automatic meter reading. So we can choose any one of the three. Among them, the prepaid electric energy metering performs best. This can be a reference for grid company＇ s decision.展开更多
Performing analytics on the load curve(LC)of customers is the foundation for demand response which requires a better understanding of customers'consumption pattern(CP)by analyzing the load curve.However,the perfor...Performing analytics on the load curve(LC)of customers is the foundation for demand response which requires a better understanding of customers'consumption pattern(CP)by analyzing the load curve.However,the performances of previous widely-used LC clustering methods are poor in two folds:larger number of clusters,huge variances within a cluster(a CP is extracted from a cluster),bringing huge difficulty to understand the electricity consumption pattern of customers.In this paper,to improve the performance of LC clustering,a clustering framework incorporated with community detection is proposed.The framework includes three parts:network construction,community detection,and CP extraction.According to the cluster validity index(CVI),the integrated approach outperforms the previous state-of-the-art method with the same amount of clusters.And the approach needs fewer clusters to achieve the same performance measured by CVI.展开更多
The transition towards a more sustainable environment requires the development of new control systems on the demand side to integrate renewable energy sources into the energy systems.For this purpose,energy meter data...The transition towards a more sustainable environment requires the development of new control systems on the demand side to integrate renewable energy sources into the energy systems.For this purpose,energy meter data of homes have been broadly used in modelling,forecast and optimal control of energy use.However,usability and reliability of household energy meter data have not been specifically addressed.In this study,we apply commonly used machine learning methods on the heating consumption data of(1)two individual homes in an apartment building and(2)the district heating substation of the apartment building which includes 72 homes,to identify how the characteristics of data affect the result of data analysis.Two clustering approaches were applied using the K-means algorithm to group similar heating daily profiles.Using the clustering results,different classification algorithms such as logistic regression and random forest were applied to predict the heating consumption level with regards to the weather conditions.The data analysis process showed that the substation data which is the aggregated heating consumption of the 72 homes is more reliable and valid for energy prediction than the data from two individual homes.This is due to the large variation and uncertainty in the daily energy use of individual homes.展开更多
The COVID-19 pandemic has had drastic effects on societies around the world.Due to restrictions or recom-mendations,companies,industries and residents experienced changes in their routines and many people shifted to w...The COVID-19 pandemic has had drastic effects on societies around the world.Due to restrictions or recom-mendations,companies,industries and residents experienced changes in their routines and many people shifted to working from home.This led to alterations in electricity consumption between sectors and changes in daily patterns.Understanding how various properties and features of load patterns in the electricity network were affected is important for forecasting the network’s ability to respond to sudden changes and shocks,and helping system operators improve network management and operation.In this study,we quantify the extent to which the COVID-19 pandemic has led to shifts in the electricity consumption patterns of different sectors in Sweden.The results show that working from home during the pandemic has led to an increase in the residential sector’s total consumption and changes in its consumption patterns,whereas there were only slight decreases in the industrial sector and relatively few changes in the public and commercial sectors.We discuss the reasons for these changes,the effects that these changes will have on expected future electricity consumption patterns,as well as the effects on potential demand flexibility in a future where working from home has become the new norm.展开更多
基金supported by the National Science and Technology Support Program of China (2015BAG10B01)the National Science Foundation of China under Grant No. 61232016, No.U1405254the PAPD fund
文摘Meter Data Collection Building Area Network(MDCBAN) deployed in high rises is playing an increasingly important role in wireless multi-hop smart grid meter data collection. Recently, increasingly numerous application layer data traffic makes MDCBAN be facing serious communication pressure. In addition, large density of meter data collection devices scattered in the limited geographical space of high rises results in obvious communication interference. To solve these problems, a traffic scheduling mechanism based on interference avoidance for meter data collection in MDCBAN is proposed. Firstly, the characteristics of network topology are analyzed and the corresponding traffic distribution model is proposed. Next, a wireless multi-channel selection scheme for different Floor Gateways and a single-channel time unit assignment scheme for data collection devices in the same Floor Network are proposed to avoid interference. At last, a data balanced traffic scheduling algorithm is proposed. Simulation results show that balanced traffic distribution and highly efficient and reliable data transmission can be achieved on the basis of effective interference avoidance between data collection devices.
文摘The choice of meter data acquisition methods has important significance for the electric energy management. Based on the comprehensive analysis of several meter data acquisition methods, this paper assess the performance of each one by analytic hierarchy process. We can draw a conclusion by calculating＂ The local automatic meter reading, the prepaid electric energy metering and the remote automatic meter reading have almost the same performance. They are better than the manual meter reading and the vehicle mounted mobile automatic meter reading. So we can choose any one of the three. Among them, the prepaid electric energy metering performs best. This can be a reference for grid company＇ s decision.
基金Supported by the Major Program of National Natural Science Foundation of China(No.61432006)。
文摘Performing analytics on the load curve(LC)of customers is the foundation for demand response which requires a better understanding of customers'consumption pattern(CP)by analyzing the load curve.However,the performances of previous widely-used LC clustering methods are poor in two folds:larger number of clusters,huge variances within a cluster(a CP is extracted from a cluster),bringing huge difficulty to understand the electricity consumption pattern of customers.In this paper,to improve the performance of LC clustering,a clustering framework incorporated with community detection is proposed.The framework includes three parts:network construction,community detection,and CP extraction.According to the cluster validity index(CVI),the integrated approach outperforms the previous state-of-the-art method with the same amount of clusters.And the approach needs fewer clusters to achieve the same performance measured by CVI.
文摘The transition towards a more sustainable environment requires the development of new control systems on the demand side to integrate renewable energy sources into the energy systems.For this purpose,energy meter data of homes have been broadly used in modelling,forecast and optimal control of energy use.However,usability and reliability of household energy meter data have not been specifically addressed.In this study,we apply commonly used machine learning methods on the heating consumption data of(1)two individual homes in an apartment building and(2)the district heating substation of the apartment building which includes 72 homes,to identify how the characteristics of data affect the result of data analysis.Two clustering approaches were applied using the K-means algorithm to group similar heating daily profiles.Using the clustering results,different classification algorithms such as logistic regression and random forest were applied to predict the heating consumption level with regards to the weather conditions.The data analysis process showed that the substation data which is the aggregated heating consumption of the 72 homes is more reliable and valid for energy prediction than the data from two individual homes.This is due to the large variation and uncertainty in the daily energy use of individual homes.
基金This study is funded by the Swedish Energy Agency(Ener-gimyndigheten),as part of the E2B2 research program(project number P2021–00187).
文摘The COVID-19 pandemic has had drastic effects on societies around the world.Due to restrictions or recom-mendations,companies,industries and residents experienced changes in their routines and many people shifted to working from home.This led to alterations in electricity consumption between sectors and changes in daily patterns.Understanding how various properties and features of load patterns in the electricity network were affected is important for forecasting the network’s ability to respond to sudden changes and shocks,and helping system operators improve network management and operation.In this study,we quantify the extent to which the COVID-19 pandemic has led to shifts in the electricity consumption patterns of different sectors in Sweden.The results show that working from home during the pandemic has led to an increase in the residential sector’s total consumption and changes in its consumption patterns,whereas there were only slight decreases in the industrial sector and relatively few changes in the public and commercial sectors.We discuss the reasons for these changes,the effects that these changes will have on expected future electricity consumption patterns,as well as the effects on potential demand flexibility in a future where working from home has become the new norm.
