HVAC-COOLING TOWER ENERGY CONSUMPTION PREDICTION IN COMMERCIAL BUILDINGS USING ADVANCED NEURAL NETWORK FRAMEWORKS
Abstract
Today, a major area of concern is the huge consumption of energy worldwide. The modern buildings are considered the largest energy consumers. When the energy consumption pattern of buildings is analyzed, attention is diverted towards the “Heating, Ventilation, and Air Conditioning” plant, which is accountable for the maximum consumption of energy in the buildings. This research work proposes a Deep learning framework including variants of Artificial Neural Network (ANN), namely, Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), and Gated Recurrent Units (GRU) to analyze and predict the abnormal energy consumption of the HVAC plants in commercial buildings. Also, proficient pre-processing, exploratory data analysis, and hyperparameter tuning for the proposed models are the key factors of this research. The proposed models have exhibited significant improvement as compared to the original RNN, LSTM, and GRU models. Different models of GRU were created using different values for hyperparameters, and the results of the experiments performed on the dataset show that one of the models of GRU outperformed the others with the least Mean Squared Errors (0.006) and (0.005) for the two months April and August, respectively. The research reveals that GRU is a better option for time series data analysis and is useful in energy consumption prediction for HVAC plants.
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References
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