International Business Machines Corporation

Youngdeok Hwang

Hendrik F Hamann

Theodore Gerard Van Kessel

Siyuan Lu

Jonathan Lenchner

Levente Klein

Fernando Jimenez Marianno

Gerald James Tesauro

Ramachandran Muralidhar

Young Min Lee

Jayant R Kalagnanam

Yada Zhu

Kyong Min Yeo

Ildar Khabibrakhmanov

Xiao Liu

Methods, systems, and computer program products for detection of an arbitrarily-shaped source of an abnormal event via use of a hierarchical reconstruction method are provided herein. A computer-implemented method includes detecting an abnormal event based on analysis of sensor data, wherein said analysis of the sensor data comprises comparing the sensor data to a user-defined threshold; generating a query based on the detected abnormal event; processing the query against one or more given data repositories; executing an inverse model using an output generated in relation to said processing to identify a source of the detected abnormal event, wherein the source comprises an arbitrary shape; and outputting the identified source of the detected abnormal event.

Detection algorithms for distributed emission sources of abnormal events

A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

Parameter-dependent model-blending with multi-expert based machine learning and proxy sites

A procedure for forecasting building energy consumption by evaluating performance of variable base degree and variable based enthalpy models. Dynamic weights are computed for the variable base degree and variable based enthalpy models and used in making future energy prediction based on weather forecast data. The weather forecast data may be corrected for bias. The variable base degree and variable based enthalpy models may be calibrated based on outlier removed historic energy consumption data and historic ambient air temperature data.

Building energy consumption forecasting procedure using ambient temperature, enthalpy, bias corrected weather forecast and outlier corrected sensor data

A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.

Multi-model blending

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White Plains, NY, United States of America

Youngdeok Hwang