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1.发明申请ESTIMATING MONTHLY HEATING OIL CONSUMPTION FROM FISCAL YEAR OIL CONSUMPTION DATA USING MULTIPLE REGRESSION AND HEATING DEGREE DAY DENSITY FUNCTION 审中-公开使用多次回归和加热度密度函数估算每年的年度油耗消耗量的月度加热油消耗量公开(公告)号：US20120278038A1
公开(公告)日：2012-11-01
申请号：US13098076
申请日：2011-04-29
申请人： Lianjun An , Huijing Jiang , Young Min Lee , Fei Liu , Estepan Meliksetian , Chandrasekhara K. Reddy
发明人： Lianjun An , Huijing Jiang , Young Min Lee , Fei Liu , Estepan Meliksetian , Chandrasekhara K. Reddy
IPC分类号： G06F19/00
CPC分类号： G06Q10/04 , F24D19/1048 , F24D19/1063 , F24D19/1081 , G06Q50/06
摘要： Estimating monthly heating oil consumption of a building that uses heating oil and non-oil source of energy, may include separating by applying statistical models, yearly consumption of oil data associated with the building into base load oil consumption and space heating oil consumption. The separating may also include determining monthly base load oil consumption associated with the building. Monthly space heating consumption of oil may be estimated by applying a heating degree day density function to the space heating oil consumption. The monthly space heating consumption may be aggregated with the monthly base load oil consumption to estimate the monthly heating oil consumption.
摘要翻译： 估计使用取暖油和非油源能源的建筑物的每月采暖油消耗量可能包括通过应用统计模型分离，将与建筑相关的石油数据的年消耗量降低为基础油耗和空间采暖油消耗量。 分离还可以包括确定与建筑物相关联的每月基础油耗。 可以通过将加热度日密度函数应用于空间加热油消耗来估计每月空间加热消耗的油。 每月空间采暖消耗量可以与每月基本负荷油耗相结合，以估算每月采暖油消耗量。
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2.发明申请ANOMALY DETECTION, FORECASTING AND ROOT CAUSE ANALYSIS OF ENERGY CONSUMPTION FOR A PORTFOLIO OF BUILDINGS USING MULTI-STEP STATISTICAL MODELING 有权异常检测，预测和根本原因分析使用多步统计建模的建筑物组合能源消耗公开(公告)号：US20120278051A1
公开(公告)日：2012-11-01
申请号：US13098044
申请日：2011-04-29
申请人： Huijing Jiang , Young Min Lee , Fei Liu
发明人： Huijing Jiang , Young Min Lee , Fei Liu
摘要： Multi-step statistical modeling in one embodiment of the present disclosure enables anomaly detection, forecasting and/or root cause analysis of the energy consumption for a portfolio of buildings using multi-step statistical modeling. In one aspect, energy consumption data associated with a building, building characteristic data associated with the building, building operation and activities data associated with the building, and weather data are used to generate a variable based degree model. A base load factor, a heating coefficient and a cooling coefficient associated with the building and an error term are determined from the variable based degree model and used to generate a plurality of multivariate regression models. A time series model is generated for the error term to model seasonal factors which reflect monthly dependence on energy use and an auto-regressive integrated moving average model (ARIMA) which reflects temporal dependent patterns of the energy use.
摘要翻译： 在本公开的一个实施例中的多步统计建模使得能够使用多步统计建模对建筑物组合的能量消耗进行异常检测，预测和/或根本原因分析。 一方面，使用与建筑物相关联的能量消耗数据，构建与建筑物相关联的特征数据，建筑物操作和与建筑物相关联的活动数据以及天气数据来生成基于变量的度模型。 从基于变量的度模型确定基础负荷因子，与建筑物相关联的加热系数和冷却系数以及误差项，并用于产生多个多元回归模型。 为误差项生成时间序列模型，以模拟每月依赖能源使用的季节因素和反映能量使用的时间依赖模式的自回归积分移动平均模型（ARIMA）。
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3.发明授权公开(公告)号：US08738334B2
公开(公告)日：2014-05-27
申请号：US13098044
申请日：2011-04-29
申请人： Huijing Jiang , Young Min Lee , Fei Liu
发明人： Huijing Jiang , Young Min Lee , Fei Liu
IPC分类号： G06F17/10
摘要： Multi-step statistical modeling in one embodiment of the present disclosure enables anomaly detection, forecasting and/or root cause analysis of the energy consumption for a portfolio of buildings using multi-step statistical modeling. In one aspect, energy consumption data associated with a building, building characteristic data associated with the building, building operation and activities data associated with the building, and weather data are used to generate a variable based degree model. A base load factor, a heating coefficient and a cooling coefficient associated with the building and an error term are determined from the variable based degree model and used to generate a plurality of multivariate regression models. A time series model is generated for the error term to model seasonal factors which reflect monthly dependence on energy use and an auto-regressive integrated moving average model (ARIMA) which reflects temporal dependent patterns of the energy use.
摘要翻译： 在本公开的一个实施例中的多步统计建模使得能够使用多步统计建模对建筑物组合的能量消耗进行异常检测，预测和/或根本原因分析。 一方面，使用与建筑物相关联的能量消耗数据，构建与建筑物相关联的特征数据，建筑物操作和与建筑物相关联的活动数据以及天气数据来生成基于变量的度模型。 从基于变量的度模型确定基础负荷因子，与建筑物相关联的加热系数和冷却系数以及误差项，并用于产生多个多元回归模型。 为误差项生成时间序列模型，以模拟每月依赖能源使用的季节因素和反映能量使用的时间依赖模式的自回归积分移动平均模型（ARIMA）。
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公开(公告)号：US08645304B2
公开(公告)日：2014-02-04
申请号：US13213273
申请日：2011-08-19
申请人： Huijing Jiang , Fei Liu , Aurelie C. Lozano
发明人： Huijing Jiang , Fei Liu , Aurelie C. Lozano
IPC分类号： G06N5/02
摘要： Structural changes in causal relationship over time may be detected, for example, by a Markov switching vector autoregressive model that detects and infers the structural changes in the causal graphs.
摘要翻译： 随着时间的推移，因果关系的结构变化可能被检测到，例如，通过马尔科夫切换向量自回归模型检测并推断因果图中的结构变化。
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公开(公告)号：US20130046721A1
公开(公告)日：2013-02-21
申请号：US13213273
申请日：2011-08-19
申请人： Huijing Jiang , Fei Liu , Aurelie C. Lozano
发明人： Huijing Jiang , Fei Liu , Aurelie C. Lozano
IPC分类号： G06N5/02
摘要： Structural changes in causal relationship over time may be detected, for example, by a Markov switching vector autoregressive model that detects and infers the structural changes in the causal graphs.
摘要翻译： 随着时间的推移，因果关系的结构变化可能被检测到，例如，通过马尔科夫切换向量自回归模型检测并推断因果图中的结构变化。
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