Land cover map can accurately characterize the spatial distribution of natural and artificial surface features.However,large-scale land cover products with submeter resolution are still scarce.To address this gap,this...Land cover map can accurately characterize the spatial distribution of natural and artificial surface features.However,large-scale land cover products with submeter resolution are still scarce.To address this gap,this study proposes an innovative data annotation engine,called initial and expanded labeling,to generate reliable labels for high-resolution imagery.The engine takes imagery and historical products as input,generates a small number of labels using weight voting in the first stage,and iteratively expands the labels in the second stage.The proposed method can effectively deal with the insufficiency of training labels in large-scale submeter land cover mapping.Based on the datasets generated by this engine,we have produced the first large-scale submeter land cover map covering the urban areas of 42 major cities in China,called EcoVision.It has a spatial resolution of about 0.5 m with 8 representative urban land cover categories.The product has been validated with 23,850,000 randomly sampled validation pixels in 42 cities and has an overall accuracy of 83.6%.Compared with 5 existing land cover maps,EcoVision shows superior performance in spatial resolution,accuracy,and details.The product has been made public,providing high-precision data support for urban sustainable development research and territorial spatial planning.展开更多
This paper analyzes the effect of the depth of submetering(i.e.,from whole-building level to system level to equipment level and further to sensor level)on the energy savings that can be achieved in energy analytics o...This paper analyzes the effect of the depth of submetering(i.e.,from whole-building level to system level to equipment level and further to sensor level)on the energy savings that can be achieved in energy analytics or energy information system(EIS)implementations.An EIS is defined as a combination of software and hardware systems that gather energy-related data,feed it into an analytics engine,and present building operators with analyses that allow them to reduce energy consumption.Data regarding the energy savings,the depth of sub-metering in the EIS implementation,and the cost of submetering was gathered for 21 case(building portfolio)studies and analyzed to determine if there is a relationship between the depth of submetering and the energy savings achieved.It was found that in general,deeper submetering does in fact appear to enable deeper energy savings.The one exception to this is sensor-level data:the addition of detailed sensor level metering to other higher levels of metering data does not seem to enable deeper energy savings in EIS implementations.Detailed findings in energy savings,cost and cost-effectiveness were presented for different levels of metering that may provide insightful rule-of-thumb estimations for similar implementations.展开更多
This paper investigates how existing forecasting models can be enhanced to accurately forecast the electric load at factory level,enabling industrial companies to shift consumption to times of low energy costs.The mod...This paper investigates how existing forecasting models can be enhanced to accurately forecast the electric load at factory level,enabling industrial companies to shift consumption to times of low energy costs.The model architecture must outperform state-of-the-art models and be sufficiently robust for use in multiple factories with low effort for specific applications.Moreover,this work focuses on the processing of high-resolution input data available almost in real time from multiple submeters after the main meters.The theory of load forecasting and related works are summarized in a first step including the requirements of forecasting models applied at factory level.Based on existing models,a new hybrid machine-learning model is proposed,combining a decision tree-based typical load profiler with a convolutional neural network that extracts features from multidimensional endogenous inputs with measurements of the preceding two weeks for multi-step-ahead load forecasts updated almost in real time.Furthermore,a multi-model approach is presented for calculating bottom-up forecasts with submeter data aggregated to a main-meter forecast.In a case study,the forecasting accuracy of the hybrid model is compared to both base models and a seasonal naïve model calculating the load forecasts for three factories.The results indicate that the proposed typical-load-profile-supported convolutional neural network for all three factories achieves the lowest forecasting error.Furthermore,it is validated that a reduction in data transfer delay leads to better forecasts,as the forecasting accuracy is higher with near real time data than with a data transfer delay of one day.Thus,a model architecture is proposed for robust forecasting in digitalized factories.展开更多
基金supported by the National Key Research and Development Program of China(grant number 2024YFF1306102)the National Natural Science Foundation of China(grant numbers 42271328 and 42471391).
文摘Land cover map can accurately characterize the spatial distribution of natural and artificial surface features.However,large-scale land cover products with submeter resolution are still scarce.To address this gap,this study proposes an innovative data annotation engine,called initial and expanded labeling,to generate reliable labels for high-resolution imagery.The engine takes imagery and historical products as input,generates a small number of labels using weight voting in the first stage,and iteratively expands the labels in the second stage.The proposed method can effectively deal with the insufficiency of training labels in large-scale submeter land cover mapping.Based on the datasets generated by this engine,we have produced the first large-scale submeter land cover map covering the urban areas of 42 major cities in China,called EcoVision.It has a spatial resolution of about 0.5 m with 8 representative urban land cover categories.The product has been validated with 23,850,000 randomly sampled validation pixels in 42 cities and has an overall accuracy of 83.6%.Compared with 5 existing land cover maps,EcoVision shows superior performance in spatial resolution,accuracy,and details.The product has been made public,providing high-precision data support for urban sustainable development research and territorial spatial planning.
文摘This paper analyzes the effect of the depth of submetering(i.e.,from whole-building level to system level to equipment level and further to sensor level)on the energy savings that can be achieved in energy analytics or energy information system(EIS)implementations.An EIS is defined as a combination of software and hardware systems that gather energy-related data,feed it into an analytics engine,and present building operators with analyses that allow them to reduce energy consumption.Data regarding the energy savings,the depth of sub-metering in the EIS implementation,and the cost of submetering was gathered for 21 case(building portfolio)studies and analyzed to determine if there is a relationship between the depth of submetering and the energy savings achieved.It was found that in general,deeper submetering does in fact appear to enable deeper energy savings.The one exception to this is sensor-level data:the addition of detailed sensor level metering to other higher levels of metering data does not seem to enable deeper energy savings in EIS implementations.Detailed findings in energy savings,cost and cost-effectiveness were presented for different levels of metering that may provide insightful rule-of-thumb estimations for similar implementations.
基金The research has received funding from the German Federal Ministry for Economic Affairs and Energy(Project number 03EI6019B-Machine learning for power load profile prediction and energy flexibility man-agement strategies).
文摘This paper investigates how existing forecasting models can be enhanced to accurately forecast the electric load at factory level,enabling industrial companies to shift consumption to times of low energy costs.The model architecture must outperform state-of-the-art models and be sufficiently robust for use in multiple factories with low effort for specific applications.Moreover,this work focuses on the processing of high-resolution input data available almost in real time from multiple submeters after the main meters.The theory of load forecasting and related works are summarized in a first step including the requirements of forecasting models applied at factory level.Based on existing models,a new hybrid machine-learning model is proposed,combining a decision tree-based typical load profiler with a convolutional neural network that extracts features from multidimensional endogenous inputs with measurements of the preceding two weeks for multi-step-ahead load forecasts updated almost in real time.Furthermore,a multi-model approach is presented for calculating bottom-up forecasts with submeter data aggregated to a main-meter forecast.In a case study,the forecasting accuracy of the hybrid model is compared to both base models and a seasonal naïve model calculating the load forecasts for three factories.The results indicate that the proposed typical-load-profile-supported convolutional neural network for all three factories achieves the lowest forecasting error.Furthermore,it is validated that a reduction in data transfer delay leads to better forecasts,as the forecasting accuracy is higher with near real time data than with a data transfer delay of one day.Thus,a model architecture is proposed for robust forecasting in digitalized factories.
