The task is to write a literature review about deep learning models for predicting building heating and cooling loads in arid climate, baised on three research gaps as explained below, and using the references attached at the end Tuning deep learning models for predicting building heating and cooling loads in arid climate Abstract

The task is to write a literature review about deep learning models for predicting building heating and cooling loads in arid climate, baised on three research gaps as explained below, and using the references attached at the end

 

 

Tuning deep learning models for predicting building heating and cooling loads in arid climate

Abstract

 

In —–, residential buildings consume more than half of the total energy. The energy consumption is even higher in regions located in the center of the country due to their arid climate. —— region, which is the seventh most populated region in the country, has an arid desert climate, known for its extremely cold winters and for extremely hot summers. To alleviate this problem, this study investigates the effect of using deep neural network models in predicting building heating and cooling loads with different tunnings of the models. The tuned models are constructed using a dataset generated by IES simulation software for —— city, the capital of the —— region. The efficiency of the constructed models is demonstrated using several performance measures. The outcomes of the research assist architects in heating and cooling loads of new energy-efficient design buildings in the pre-design stage.

 

 

Related work

Research gap

  • Most of the current studies of energy consumption in buildings do not focus on building characteristics as predictors.

Examples of studies that have been excluded are [50]–[56], all of which were not based on building characteristics to predict energy consumption. Instead, they utilized historical data of energy consumption [53]–[56] or combined historical data with types of data like climate data [50]–[52]. In [50], the authors predicted HVAC energy consumption based on historical weather, consumption, and occupancy data; [51] predicted cooling load based on climate prediction parameters and daily and weekly energy usage ; [52] predicted the total energy consumption prediction based on energy consumption and climate data;[53] proposed an energy consumption prediction model based on historical energy consumption in kiloWatt (kW) in the period of December 2006 to November 2010 with one-minute interval;[54] suggested a model based on thermal energy consumption data with five minute-interval from a large number of smart meters

deployed in 12 buildings; [55] proposed a prediction model based on the hourly electric demand intensity in two buildings; while [56] predicted the hourly electric demand based on multi-dimensional attributes of 100 days.

 

-Paraphrase the highlighted text.

-Refer to the attached references as they mentioned in the text.

-Elaborated more based on the research gap (Most of the current studies of energy consumption in buildings do not focus on building characteristics as predictors.)

-and more studies which are not focusing on building characteristics to highlight the gap

 

 

  • Most of the studies depend on one dataset for prediction published by Tsanas and Xifara [57].

A large proportion of papers (13 out of 33 papers) used the benchmarked energy efficiency dataset provided by Tsanas and Xifara [57], which contains 768 instances and is available at  https://archive.ics.uci.edu/ml/datasets/Energy+efficiency, with its first 25 records shown in Fig.  14. This dataset contains eight building characteristics as predictors for buildings’ energy consumption (X1-X8):  Relative Compactness, Surface Area, Wall Area, Roof Area, Overall Height, Orientation, Glazing Area, Glazing Area, Distribution. The dataset contains two target variables (Y1 and Y2): Heating Load and Cooling Load.

 

-Paraphrase the highlighted text.

-Refer to the attached references as they mentioned in the text.

-Elaborated more based on the research gap(Most of the studies depend on one dataset for prediction published by Tsanas and Xifara [57])as this research using self-generated dataset

– and more studies which are not listed in the text(more about the ones which didn’t focus on building characteristic

 

  • Most of the current studies of energy consumption models used machine learning methods, while deep learning rarely used.

In [38], a deep neural network was proposed for forecasting heating and cooling loads of buildings depending on various buildings’ parameters. A deep neural network with sensitivity analysis (DNN-SA) model was presented in [39] to forecast heating and cooling loads for a variety of structures, as shown in Fig.  21. The DNNs proposed in this research include multi-layer perceptron (MLP) networks, and all the models were developed using extensive testing with many layers. The sensitivity analysis was used as a post-processing technique to extract information from the trained model. The study reported in [58] proposed a two-layer and three-layer deep LSTM models for heating cooling prediction models. The results show that deep learning increases the model’s performance when compared to simple ANN models.

 

-Paraphrase the highlighted text.

-Refer to the attached references as they mentioned in the text.

-Elaborated more based on the research gap(Most of the current studies of energy consumption models used machine learning methods, with e deep learning rarely used.)

– only three studies which used deep learning based on building characteristics which means ts limited , explain more

 

 

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