Studied the trend of overall load and divided it into three classes, low, high and average efficient labels. Then trained a deep learning model using KerasClassifier and three-layered baseline model with relu and softmax as activation functions to predict the label in Python Jupiter book Built explanatory deep learning model to predict the house heating and cooling load using tensor-flow and keras with 93% accuracy
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Studied the trend of overall load and divided it into three classes, low, high and average efficient labels. Then trained a deep learning model using KerasClassifier and three-layered baseline model with relu and softmax as activation functions to predict the label in Python Jupiter book Built explanatory deep learning model to predict the house heating and cooling load using tensor-flow and keras with 93% accuracy