Artificial Intelligent Machine Learning and Big Data Mining of Desert Geothermal Heat Pump: Analysis, Design and Control

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Author(s)

Murad Al Shibli 1,* Bobby Mathew 2

1. Head of Artificial Intelligent and Autonomous Systems Engineering Technology Program, Project Manager of Joint Aviation Command (JAC) Program, Abu Dhabi Polytechnic, IAT, Abu Dhabi, UAE

2. Mechanical Engineering Department, College of Engineering, United Arab Emirates University (UAEU)

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2019.04.01

Received: 15 Oct. 2018 / Revised: 26 Dec. 2018 / Accepted: 13 Feb. 2019 / Published: 8 Apr. 2019

Index Terms

Artificial intelligent systems, big data, machine learning, geothermal heat pump, domestic cooling system, switch-on-switch-off control

Abstract

Nowadays sustainable underground geothermal energy resources have received special attention thanks for being characterized as clean, zero-carbon footprint, reliable, and free source of renewable energy that can run all year long and around the clock. Barren desert lands, which make up 33% and contribute to almost 30 Million km² of global land surface area, is increasingly seen as supply of green energy but not yet efficiently and globally utilized although it can save up to 70% compared to traditional HVAC systems bills. This paper presents a novel artificial intelligent machine learning and big data algorithm to analyze and control geothermal heat pump system (GHP). In particular, the main objective of this research is to model, design, analyze, control and optimize the performance of desert underground GTH system based on thermodynamics laws and AI machine learning. As a case study, the analysis and design of desert GHP is performed based on the annual weather data collected for Al Ain city in UAE. By selecting a horizontal layout, the design analysis results show that GHP unit needs a 66 m total trench length with a cooling capacity estimated of 12.4 kW, heat pump COP of 2.8 and 1.6 for the system COP with 30.3 L/min water flow rate. Similar results for the heating system are obtained as well. Furthermore, financial calculations show the GHP system is very economic and competitive comparing with the traditional cooling/heating systems. It is figured out that the annual cost of the GHP system costs around $1676 compared with $7992 if air-cooled chiller and boiler are used. To maintain the geothermal system for one life cycle (usually 20 years) it needs to spend only $14,659 compared with $109,944 in case HVAC system is utilized. The overall life cycle cost in case of the desert GHP system does not exceed (45%) of the traditional HVAC system ($81,881 compared to $181,974). One of the direct applications is use this proposed desert GHP to cool the roof water tank for domestic and personal usage. Furthermore, artificial intelligent and big data machine learning is executed to analyze the weather conditions related to the GHP performance based on huge number of thermal observations recorded for the years 2015-2018. Moreover, the mean switch-off control hours of the GHP is examined by developing a supervised learning predictive model. For the purpose of validation a four ton Bosch GHP unit is selected as a benchmark. Switch-off control hours per month for the entire geothermal data set are demonstrated by using a linear regression model that help to guide the controller to switch-on/switch-off the system without having the need for the real data measurement. One primary outcome obtained is the ability to optimize the GHP performance, save primary input energy and operation periods. Furthermore, the results interprets that almost one third of the year is in a switched-off saving mode (33%), compared to 67% in switch-on mode. This smart big data control will lead to a life-cycle saving of $27,020. This AI saving strategy is found to be competitive and leading compared to other schemes. It is worthy to recommend linking GHP controller with real-time radar or weather station that will fed the system with real data conditions which would lead to improving its performance and dispense costly measuring sensors.

Cite This Paper

Murad Al Shibli, Bobby Mathew, "Artificial Intelligent Machine Learning and Big Data Mining of Desert Geothermal Heat Pump: Analysis, Design and Control", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.4, pp.1-13, 2019. DOI:10.5815/ijisa.2019.04.01

Reference

[1]Enrico Barbier, “Nature and technology of geothermal energy: A review,” Renewable and Sustainable Energy Reviews, Elsevier, Volume 1, Issues 1-2, , Pages 1-69, March-June 1997.
[2]Lund, John W., 1988. “Geothermal Heat Pump Utilization in the United States,” Geo-Heat Center Quarterly Bulletin, Vol. 11, No. 1, Klamath Falls, OR.
[3]Bruce D. Green and R. Gerald Nix, Geothermal—The Energy Under Our Feet Geothermal Resource Estimates for the United States, technical report, National Renewable Energy Laboratory, Colorado, 2006.
[4]Alyssa Kagel, Diana Bates, & Karl Gawell, A Guide to Geothermal Energy and the Environment, Geothermal Energy Association, 2006.
[5]San Diego Regional Renewable Energy Study Group, Chapter 5: Potential for Renewable Energy in the San Diego Region, The Center for Energy Efficiency & Renewable Technologies, April 7, 2005.
[6]Liz Battocletti, Geothermal Small Business Workbook, Bob Lawrence & Associates, Inc., U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Geothermal Technologies Program, May 2003.
[7]John Sass and Sue Priest, Geothermal California, U.S. Geothermal Development, October/September 2002.
[8]McQuay International, Geothermal Heat Pump Design Manual, Application Guide AG 31-008, 2002.
[9]Masashi Shibaki, Fredric Beck, Geothermal Energy for Electric Power A REPP Issue Brief, Renewable Energy Policy Project, December 2003.
[10]Y. A. Cengel, M. A. Boles, Thermodynamics: an Engineering Approach, Fifth Edition, McGraw Hill, 2006.
[11]Silvia Cocchi, Sonia Castellucci, and Andrea Tucci, Modeling of an Air Conditioning System with Geothermal Heat Pump for a Residential Building, Mathematical Problems in Engineering, Volume 2013, Article ID 781231, 6 pages, Hindawi Publishing Corporation, 2013.
[12]M. Fathizadeh, Daniel Seim, Design and Implementation of Geothermal Systems for Heating and Air Conditioning, Proceedings of the World Congress on Engineering and Computer Science 2013, Vol I (WCECS 2013), San Francisco, USA, 23-25 October, 2013.
[13]Gaffar G. Momin, Experimental Investigation Of Geothermal Air Conditioning, American Journal of Engineering Research (AJER), Volume-02, Issue-12, pp-157-170, 2013.
[14]K. S. Leea, E. C. Kangb, , M. Ghorabc, L. Yangc, E. Entchevc, E. J. Leea, Smart Building Heating, Cooling and Power Generation with Solar Geothermal Combined Heat Pump System, 12th IEA Heat Pump Conference 2017, Roterdam, Holland, 2017.
[15]Sneha Shahare, T. Harinarayana, Energy Efficient Air Conditioning System Using Geothermal Cooling-Solar Heating in Gujarat, India, Journal of Power and Energy Engineering, 4, 57-71, 2016.
[16]Jaai Prakash Badgujar, Dheeraj Dilip Kulkarni, Sharique Ali Ahmad, Fauzia Siddiqui, Paramjit Thakur, Ground Coupled Heat Exchanger Air Conditioning System: Case Study, International Journal of Scientific & Engineering Research, Volume 8, Issue 3, March-2017.
[17]Manan Shah, Anirbid Sircar, Karan Patel, Nahid Shaikh, Vivek Thakar, Dwijen Vaidya, Shishir Chandra, Comprehinsive Study on Hybrid Geothermal-Solar Cooling Systems with Special Focus on Gujarar, Western India, 43rd Proceeding on Workshop on Geothermal Reservoir Engineering, Stanford University, February 12-14, 2018.
[18]Sanjay N. Mali, Ashok B. More, D. S. patil, Application of Geothermal Cooling Techniques to Improve Thermal Conditions of a Residential Building, International Journal of Civil and Structural Engineering Research, Vol. 2, Issue 1, pp: (158-161), 2014.
[19]Nimish Dhepe, Raahul Krishna, A Review of the Advancements in Geothermal Heating and Cooling System, Journal of Alternate Energy Sources and Technologies, Volume 8, Issue 1, 2018.
[20]Rahul Vadher, Hiren Prajapati, Geothermal Air Conditioning, International Journal of Engineering Sciences & Resrarch Technology, 4(10): October, 2015.
[21]Seyed Houman Razavi, R. Ahmadi, and Alireza Zahedi, Modeling, Simulation and Dynamic Control of Solar Assisted Ground Source Heat Pump to Provide Heating Load and DHW, Applied Thermal Engineering Journal, Elsevier, Volume 129, Pages 127-144, 25 January 2018.
[22]Elisa Moretti, Emanuele Bonamente, Cinzia Buratti and Franco Cotana, Development of Innovative Heating and Cooling Systems Using Renewable Energy Sources for Non-Residential Buildings, Energies Journal, 6, 5114-5129, 2013.
[23]Alexandra L’Heureux, Katarina Grolinger, Hany F. ElYamany, Miriam A. M. Capretz, Machine Learning with Big Data: Challenges and Approaches, IEEE Access Journal, Vol. 5, 2017.
[24]Abdelladim Hadioui, Nour-eddine El Faddouli, Yassine Benjelloun Touimi, and Samir Bennani, Machine Learning Based On Big Data: Extraction of Massive Educational Knowledge, International Journal of Emerging Technologies in Learning (iJET), Vol. 12, No. 11, 2017.
[25]Mehdi Gheisari, Guojun Wang, Md Zakirul cience and Engineering (CSE) and IEEE Int. Conf. on Embedded and Ubiquitous Computing (EUC), Guangzhou, China, 2017.
[26]D. Saidulu, Machine Learning and Statistical Approaches for Big Data: Issues, Challenges and Research Directions, International Journal Alam Bhuiyan, A Survey on Deep Learning in Big Data, IEEE International Conference on Computational Soft Applied Engineering Research ISSN 0973-4562 Volume 12, pp. 11691-11699, Number 21, 2017.
[27]Junfei Qiu, Qihui Wu, Guoru Ding, Yuhua Xu and Shuo Feng, A survey of machine learning for big data Processing, EURASIP Journal on Advances in Signal Processing, 2016.
[28]Dezhi Fang and Duen Horng Chau, M3: Scaling Up Machine Learning via Memory Mapping, ACM SIGMOD/PODS, San Francisco, CA, USA, 2016.
[29]Chandrima Roy, Siddharth Rautaray, and Manjusha Pandey, Big Data Optimization Techniques: A Survey, I.J. Information Eng. and Electronic Business, 4, 41-48, MECS, 2018.
[30]Abdur Rahman, M.N.A. Khan, An Assessment of Data Mining Based CRM Techniques for Enhancing Profitability, I.J. Education and Management Engineering, 2, 30-40, 2017.
[31]Soroush Rezvanbehbahani, Leigh A. Stearns, Amir Kadivar, J. Doug Walker, C. J. van der Veen. Predicting the Geothermal Heat Flux in Greenland: A Machine Learning Approach. Geophysical Research Letters, 2017.
[32]Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems, Applied Energy, Elsevier, vol. 221(C), pages 16-27, 2018.
[33]Romana Markovic1, Caroline Lorz1, Jerome Frisch, Christoph van Treeck, Application of Support Vector Machines for Predicting the Performance of Air-Source Domestic Hot Water Heat Pump Systems, Building Simulation Proceedings of the 15th IBPSA Conference, San Francisco, CA, USA, Aug. 7-9, 2017.
[34]Bosch Thermotechnology Corp., Bosch Geothermal Heat Pumps for Residential Applications, Manual, 2013.