Home Thermal Modeling: Cooling Energy Consumption and Costs in Saudi Arabia

Objectives: The consumption of electricity and its costs are expected to be increased in Saudi Arabia due to its rapid growth in population. As the Kingdom is characterized by extreme hot climate, a massive amount of electricity consumed by the residential sector goes to power air conditioners. To control this huge amount of energyconsumedin homes, thermal models have been generated with two or more parameters. Methodology: The households’ surveys have been conducted in order to collect the data. The Non-linear regression analysis has been carried out to obtain the outcomes of study. Moreover, household surveys have been conducted for data collection. The grid algorithm and the non-linear regression have been used to learn the parameters in the model to simulate the weather in Saudi Arabia. The temperature loggers have been placed in the houses to observe the behavior of residents of using cooling system. The web forecast has been used to analyze the temperature of cities on hourly basis. Results: Simple thermal model has been built using two parameters by applying the grid and non-linear regression methods for data fitting. Then the thermal model with envelope has also been created using four parameters by applying non-linear regression method for data fitting. Conclusion: It has been evaluated through outcomes that thermal model with envelope is better as compared to simple thermal model. Moreover, the data fitting by non-linear regression method has also been observed to perform better than data fitting by grid method.

[1]  Maizura Mokhtar,et al.  Multi-agent Gaussian Adaptive Resonance Theory Map for building energy control and thermal comfort management of UCLan's WestLakes Samuel Lindow Building , 2014 .

[2]  A. T. D. Perera,et al.  A novel simulation based evolutionary algorithm to optimize building envelope for energy efficient buildings , 2014, 7th International Conference on Information and Automation for Sustainability.

[3]  Sylvain Robert,et al.  State of the art in building modelling and energy performances prediction: A review , 2013 .

[4]  J. Mallick.  Land Characterization Analysis of Surface Temperature of Semi-Arid Mountainous City Abha, Saudi Arabia Using Remote Sensing and GIS , 2014 .

[5]  Mohammad Valipour,et al.  Application of new mass transfer formulae for computation of evapotranspiration , 2014 .

[6]  Nicholas R. Jennings,et al.  Adaptive home heating control through Gaussian process prediction and mathematical programming , 2011 .

[7]  J. Féménias.  Goodness of fit: analysis of residuals , 2003 .

[8]  Philip Haves,et al.  Model predictive control for the operation of building cooling systems , 2010, Proceedings of the 2010 American Control Conference.

[9]  Muhd Zaimi Abd Majid,et al.  A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries) , 2015 .

[10]  Organisation for Economic Cooperation and Development,et al.  Organisation for economic cooperation and development , 1998 .

[11]  Mohammad Ali Gholami Sefidkouhi,et al.  Estimation of reference evapotranspiration using multivariate fractional polynomial, Bayesian regression, and robust regression models in three arid environments , 2017, Applied Water Science.

[12]  Mohammad Valipour,et al.  Analysis of potential evapotranspiration using limited weather data , 2017, Applied Water Science.

[13]  Mohammad Valipour,et al.  Future of agricultural water management in Africa , 2015 .

[14]  Prabir Barooah,et al.  Issues in identification of control-oriented thermal models of zones in multi-zone buildings , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[15]  Hani Hagras,et al.  An intelligent agent based approach for energy management in commercial buildings , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[16]  Ying Guo,et al.  A Simulator for Self-Adaptive Energy Demand Management , 2008, 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems.

[17]  Mohammad Valipour.  Ability of Box-Jenkins Models to Estimate of Reference Potential Evapotranspiration (A Case Study: Mehrabad Synoptic Station, Tehran, Iran) , 2012 .

[18]  H J Motulsky,et al.  Fitting curves to data using nonlinear regression: a practical and nonmathematical review , 1987, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[19]  Raphaël Kuate,et al.  Calibration of building thermal models using an optimal control approach , 2014 .

[20]  Harry Boyer,et al.  A thermal model for phase change materials in a building roof for a tropical and humid climate: Model description and elements of validation , 2014 .

[21]  Lekshmi Vijayan Krishna.  Long Term Temperature Trends in Four Different Climatic Zones of Saudi Arabia , 2014 .

[22]  Mohammad Valipour,et al.  Calibration of mass transfer-based models to predict reference crop evapotranspiration , 2017, Applied Water Science.