Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Chemosphere

In digital era, energy efficient building remains a hot research topic because of increasing concern regarding their environmental impact and energy consumption. Designing a suitable energy efficient building based on their layout namely overall areas, distribution of the glazing areas, orientation, height, and relative compactness. Such components directly impact the heating load (HL) and cooling load (CL) of residential buildings. A precise predicting of load facilitates effective management of energy consumption and improves the quality of life. Lately, several studies have been implemented to predict the CL and HL. The most significant and challenging parts of predictive are defining the effective input parameter and developing a higher accuracy predictive model. The accuracy of predictive model based on machine learning algorithm must be enhanced by hybrid model. With this motivation, this article introduces an Improved Harris Hawks Optimization with Hybrid Deep Learning Based Heating and Cooling Load Prediction (IHHOHDL-HCLP) model on Residential Buildings. The major aim of the IHHOHDL-HCLP model is to determine the CL and HL to accomplish effective energy utilization. To accomplish this, the IHHOHDL-HCLP primarily pre-processes the raw data in two levels namely min-max normalization and polynomial equation. In addition, the HDL model involves convolutional neural network (CNN) along with long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for HL and CL prediction process. Finally, the IHHO technique was applied for fine-tuning the hyperparameters related to the DL model. The IHHOHDL-HCLP model has demonstrated maximum prediction results with low RMSE values of 0.00874 and 0.00821, respectively, when applied to HL and CL, respectively. The experimental result analysis of the IHHOHDL-HCLP model demonstrates the better performance of the IHHOHDL-HCLP model over other DL models.

Kavitha R J, Thiagarajan C, Priya P Indira, Anand A Vivek, Al-Ammar Essam A, Santhamoorthy Madhappan, Chandramohan P


Cooling load, Deep learning, Energy consumption, Heating load, Predictive models, Residential buildings