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Assessing the implications of submetering with energy analytics to building energy savings

能源与人工环境(英文) 2020年第1期1卷 27-35页

作者： Zhiqiang(John)Zhai Andrea Salazar Department of Civil Environmentaland Architectural EngineeringUniversity of Colorado at BoulderBoulderCO80309USA

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)*** 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 *** 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 *** was found that in general,deeper submetering does in fact appear to enable deeper energy *** 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 *** 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.

关键词： submetering Energy analytics Energy savings Cost effectiveness

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Typical load profile-supported convolutional neural network for short-term load forecasting in the industrial sector

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能源与人工智能(英文) 2021年第3期5卷 242-254页

作者： Thilo Walser Alexander Sauer Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstraße 1270569 StuttgartGermany Institute for Energy Efficiency in Production EEP University of StuttgartNobelstraße 1270569 StuttgartGermany

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 *** model architecture must outperform state-of-the-art models and be sufficiently robust for use in multiple factories with low effort for specific ***,this work focuses on the processing of high-resolution input data available almost in real time from multiple submeters after the main *** theory of load forecasting and related works are summarized in a first step including the requirements of forecasting models applied at factory *** 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 ***,a multi-model approach is presented for calculating bottom-up forecasts with submeter data aggregated to a main-meter *** 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 *** results indicate that the proposed typical-load-profile-supported convolutional neural network for all three factories achieves the lowest forecasting ***,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 ***,a model architecture is proposed for robust forecasting in digitalized factories.

关键词： Electricity Machine learning Load forecasting Hybrid model Industrial energy systems Energy flexibility submetering Real time data

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