Siemens Building Technologies, Inc.

Kenneth Lemke

Osman Ahmed

A system for forecasting predicted thermal loads for a building comprises a thermal condition forecaster for forecasting weather conditions to be compensated by a building environmental control system and a thermal load predictor for modeling building environmental management system components to generate a predicted thermal load for a building for maintaining a set of environmental conditions. The thermal load predictor of the present invention is a neural network and, preferably, the neural network is a recurrent neural network that generates the predicted thermal load from short-term data. The recurrent neural network is trained by inputting building thermal mass data and building occupancy data for actual weather conditions and comparing the predicted thermal load generated by the recurrent neural network to the actual thermal load measured at the building. Training error is attributed to weights of the neurons processing the building thermal mass data and building occupancy data. Iteratively adjusting these weights to minimize the error optimizes the design of the recurrent neural network for these non-weather inputs.

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Sturtevant, WI, United States of America

Kenneth Lemke