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...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.展开更多
Radiation detectors, such as survey meters, are essential for ensuring radiation safety in various sectors, including healthcare, industrial processing, emergency response, etc. However, regular calibration and proper...Radiation detectors, such as survey meters, are essential for ensuring radiation safety in various sectors, including healthcare, industrial processing, emergency response, etc. However, regular calibration and proper maintenance of survey meters are important in order to ascertain their accuracy and reliability. This study provides a comprehensive retrospective assessment of the calibration behaviour, durability, and fault trends of 160 survey meters, spanning ten different models. They were calibrated at the Secondary Standard Dosimetry Laboratory (SSDL) in Nigeria over a decade (2012-2023) using an X-Ray Beam Irradiator Model X80-225K and Cs-137 irradiator (OB6) with a PTW reference spherical chamber traceable to the IAEA SSDL in Seibersdorf, Austria. The calibration stability of each model was evaluated, revealing that models like Instrument A and Instrument B demonstrated high reliability with calibration factors close to the ideal value of 1, while models like Instrument C exhibited higher variability, suggesting less consistent performance for dose rate monitoring. Fault analysis showed that the most common issues were related to the battery compartment, indicating a need for improved handling practices. Correlation analysis reveals no statistically significant correlation between calibration factor and age of survey meter across the analysed models. The study concludes that regular calibration, proper handling, and user training are crucial for maintaining the accuracy and longevity of radiation detectors.展开更多
Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids.It offers substantial benefits across social,environmental,and economic dimensions.To effectively r...Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids.It offers substantial benefits across social,environmental,and economic dimensions.To effectively realize these advantages,a fine-grained collection and analysis of smart meter data is essential.However,the high dimensionality and volume of such time-series present significant challenges,including increased computational load,data transmission overhead,latency,and complexity in real-time analysis.This study proposes a novel,computationally efficient framework for feature extraction and selection tailored to smart meter time-series data.The approach begins with an extensive offline analysis,where features are derived from multiple domains—time,frequency,and statistical—to capture diverse signal characteristics.Various feature sets are fused and evaluated using robust machine learning classifiers to identify the most informative combinations for automated appliance categorization.The bestperforming fused features set undergoes further refinement using Analysis of Variance(ANOVA)to identify the most discriminative features.The mathematical models,used to compute the selected features,are optimized to extract them with computational efficiency during online processing.Moreover,a notable dimension reduction is secured which facilitates data storage,transmission,and post processing.Onward,a specifically designed LogitBoost(LB)based ensemble of Random Forest base learners is used for an automated classification.The proposed solution demonstrates a high classification accuracy(97.93%)for the case of nine-class problem and dimension reduction(17.33-fold)with minimal front-end computational requirements,making it well-suited for real-world applications in smart grid environments.展开更多
The purpose of this study is to evaluate the price and composition of blood glucose meters and blood glucose test papers in Hubei Province,so as to improve the affordability of blood glucose monitoring for diabetes pa...The purpose of this study is to evaluate the price and composition of blood glucose meters and blood glucose test papers in Hubei Province,so as to improve the affordability of blood glucose monitoring for diabetes patients.Using the standard survey method jointly developed by the World Health Organization(WHO)and Health Action International(HAI)for drug price composition analysis,central level data was collected through literature review,policy review,and interviews with key informants.Field research was conducted in Wuhan and Yichang to trace price data at various levels of the supply chain.At the central level,data shows that China has implemented multiple policies related to the management and pricing of blood glucose meters and test strips,but has not yet introduced price restrictions for blood glucose meters and test strips;At the supply chain level,data shows that the price composition of blood glucose meters and test strips in Hubei Province is mainly composed of manufacturer prices and retail markups,followed by supply chain taxes and wholesale markups.It can be seen from this that China has established a comprehensive medical device registration,sales,quality control and management standard system through a series of policies.At present,China has not issued price policies for blood glucose meters and blood glucose test papers.Therefore,we can reduce product prices by including medical insurance,government subsidies,competitive bidding,and reducing supply chain taxes,thus improving the affordability of blood glucose monitoring for diabetes patients.展开更多
Further development of earthquake equipments is closely associated with that of computer technology. Because Embedded PC104 module has the equivalent functions of PC,it has been widely used in recent years,and can pro...Further development of earthquake equipments is closely associated with that of computer technology. Because Embedded PC104 module has the equivalent functions of PC,it has been widely used in recent years,and can provide a new and flexible hardware design environment,but its applications in observation instruments of earth-quake precursor are rare. The present paper introduces in detail the realization of a networked geo-electrical meter by applying the low price,high reliability embedded PC104 industrial computer.展开更多
基金supported by the Spanish Ministry of Science and Innovation under Projects PID2022-137680OB-C32 and PID2022-139187OB-I00.
文摘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.
文摘Radiation detectors, such as survey meters, are essential for ensuring radiation safety in various sectors, including healthcare, industrial processing, emergency response, etc. However, regular calibration and proper maintenance of survey meters are important in order to ascertain their accuracy and reliability. This study provides a comprehensive retrospective assessment of the calibration behaviour, durability, and fault trends of 160 survey meters, spanning ten different models. They were calibrated at the Secondary Standard Dosimetry Laboratory (SSDL) in Nigeria over a decade (2012-2023) using an X-Ray Beam Irradiator Model X80-225K and Cs-137 irradiator (OB6) with a PTW reference spherical chamber traceable to the IAEA SSDL in Seibersdorf, Austria. The calibration stability of each model was evaluated, revealing that models like Instrument A and Instrument B demonstrated high reliability with calibration factors close to the ideal value of 1, while models like Instrument C exhibited higher variability, suggesting less consistent performance for dose rate monitoring. Fault analysis showed that the most common issues were related to the battery compartment, indicating a need for improved handling practices. Correlation analysis reveals no statistically significant correlation between calibration factor and age of survey meter across the analysed models. The study concludes that regular calibration, proper handling, and user training are crucial for maintaining the accuracy and longevity of radiation detectors.
文摘Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids.It offers substantial benefits across social,environmental,and economic dimensions.To effectively realize these advantages,a fine-grained collection and analysis of smart meter data is essential.However,the high dimensionality and volume of such time-series present significant challenges,including increased computational load,data transmission overhead,latency,and complexity in real-time analysis.This study proposes a novel,computationally efficient framework for feature extraction and selection tailored to smart meter time-series data.The approach begins with an extensive offline analysis,where features are derived from multiple domains—time,frequency,and statistical—to capture diverse signal characteristics.Various feature sets are fused and evaluated using robust machine learning classifiers to identify the most informative combinations for automated appliance categorization.The bestperforming fused features set undergoes further refinement using Analysis of Variance(ANOVA)to identify the most discriminative features.The mathematical models,used to compute the selected features,are optimized to extract them with computational efficiency during online processing.Moreover,a notable dimension reduction is secured which facilitates data storage,transmission,and post processing.Onward,a specifically designed LogitBoost(LB)based ensemble of Random Forest base learners is used for an automated classification.The proposed solution demonstrates a high classification accuracy(97.93%)for the case of nine-class problem and dimension reduction(17.33-fold)with minimal front-end computational requirements,making it well-suited for real-world applications in smart grid environments.
基金Hubei Provincial Medical Institutions at all levels of basic drugs and drug bridging support policy projectsResearch on the Integrated Development of Disease Prevention and Control and Medical Services in the XPCC under the Background of Healthy China (Grant No. BTJKJ2024003)。
文摘The purpose of this study is to evaluate the price and composition of blood glucose meters and blood glucose test papers in Hubei Province,so as to improve the affordability of blood glucose monitoring for diabetes patients.Using the standard survey method jointly developed by the World Health Organization(WHO)and Health Action International(HAI)for drug price composition analysis,central level data was collected through literature review,policy review,and interviews with key informants.Field research was conducted in Wuhan and Yichang to trace price data at various levels of the supply chain.At the central level,data shows that China has implemented multiple policies related to the management and pricing of blood glucose meters and test strips,but has not yet introduced price restrictions for blood glucose meters and test strips;At the supply chain level,data shows that the price composition of blood glucose meters and test strips in Hubei Province is mainly composed of manufacturer prices and retail markups,followed by supply chain taxes and wholesale markups.It can be seen from this that China has established a comprehensive medical device registration,sales,quality control and management standard system through a series of policies.At present,China has not issued price policies for blood glucose meters and blood glucose test papers.Therefore,we can reduce product prices by including medical insurance,government subsidies,competitive bidding,and reducing supply chain taxes,thus improving the affordability of blood glucose monitoring for diabetes patients.
基金"The Study of ELF Receiver"from Ministry of Science and Technology (2001BA601B03-01-03).
文摘Further development of earthquake equipments is closely associated with that of computer technology. Because Embedded PC104 module has the equivalent functions of PC,it has been widely used in recent years,and can provide a new and flexible hardware design environment,but its applications in observation instruments of earth-quake precursor are rare. The present paper introduces in detail the realization of a networked geo-electrical meter by applying the low price,high reliability embedded PC104 industrial computer.
