International Business Machines Corporation

Globalfoundries Inc.

Young T Chae

Young Min Lee

Lianjun An

Devendra K Sadana

Jeehwan Kim

Fei Liu

Rui Zhang

Augustin J Hong

Raya Horesh

Chandrasekhara K Reddy

The electrical output efficiency of a photovoltaic thermal system can be restored from degradation due to light exposure by annealing a photovoltaic thermal cell at an elevated temperature. The elevated temperature at the photovoltaic thermal cell can be provided by redirecting the flow of a heat exchange fluid to bypass a heat exchanger unit. A boiler unit may be employed to provide additional heating of the heat exchange fluid during the anneal. Further, a variable configuration lid can be provided over a front surface of the photovoltaic thermal cell to control ventilation over the front surface. During the anneal, the position of the variable configuration lid can be set so as to trap heat above the front surface and to elevate the anneal temperature further.

Efficiency restoration in a photovoltaic cell

A predictive model for building energy consumption may be constructed and run to predict energy consumption in a building or to detect anomaly in energy consumption in a building or combinations thereof. Historic energy consumption data associated with energy consumed in a building may be received. Enthalpy of air outside the building may be determined. An energy consumption model may be calibrated based on the historic energy consumption data and the enthalpy of air outside the building. The energy consumption model incorporates enthalpy difference between a balance enthalpy and the enthalpy of air outside the building.

Constructing and calibrating enthalpy based predictive model for building energy consumption

A static heat transfer model is derived from a system of dynamic equations by integrating the dynamic equations over different time periods. That static heat transfer model links periodic (e.g., monthly) energy usage with cooling and heating degree hours, humidifying and dehumidifying hours. Its coefficients of measuring correlations correspond to the thermal parameters of buildings. Temporal data from a building may be used to estimate the overall heat transfer parameters. A clustering scheme may be developed to decompose all the buildings into different clusters based on one or more similarity criteria. The overall heat transfer parameters are separated into values for the wall, roof and window using multiple buildings' data in the same cluster or group.

Estimating building thermal properties by integrating heat transfer inversion model with clustering and regression techniques for a portfolio of existing buildings

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Yorktown Heights, NY, United States of America

Young T Chae