Artificial Intelligent Machine Learning and Big Data Mining of Desert Geothermal Heat Pump: Analysis, Design and Control
Artificial Intelligent Machine Learning and Big Data Mining of Desert Geothermal Heat Pump: Analysis, Design and Control
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沙漠地源热泵人工智能机器学习与大数据挖掘：分析、设计与控制
DOI：
10.5815/ijisa.2019.04.01
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
B. Mathew
中科院分区：
文献类型：
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作者：
M. Shibli;B. Mathew
如今，可持续地下地热资源因其清洁、零碳足迹、可靠、可全年和24小时运行的免费可再生能源等特点而受到特别关注。占全球陆地面积近3000万平方公里的贫瘠沙漠占33%，越来越多地被视为绿色能源的供应，但尚未得到有效的全球利用，尽管与传统的暖通空调系统相比，它可以节省高达70%的费用。提出了一种新颖的人工智能机器学习和大数据算法来分析和控制地源热泵系统。特别是，本研究的主要目的是基于热力学定律和人工智能机器学习对沙漠地下GTH系统的性能进行建模、设计、分析、控制和优化。以阿联酋艾因市的年度气象资料为例，对沙漠热泵系统进行了分析和设计。通过选择水平布置，设计分析结果表明：地源热泵机组需要66m总沟长，预计制冷量为12.4kW，热泵COP为2.8，系统COP为1.6，流量为30.3 L/min。对于加热系统，也得到了类似的结果。此外，财务计算表明，与传统的制冷/供暖系统相比，地源热泵系统具有很强的经济性和竞争力。经计算，采用风冷冷水机组和锅炉的地源热泵系统的年成本约为1,676美元，而使用风冷冷水机组和锅炉的成本为7,992美元。要将地热系统维护一个生命周期(通常是20年)，只需花费14,659美元，而如果使用暖通空调系统，则需要花费109,944美元。沙漠地源热泵系统的总生命周期成本不超过传统暖通空调系统的45%(81,881美元与181,974美元)。其中一个直接应用是使用这种拟议的沙漠热泵来冷却屋顶水箱，供家庭和个人使用。此外，基于2015-2018年的大量热力观测记录，执行人工智能和大数据机器学习来分析与GHP性能相关的天气条件。此外，通过建立有监督的学习预测模型，考察了GHP的平均关断控制小时数。为了验证的目的，选择了一台四吨的博世GHP机组作为基准。整个地热数据集的每月关闭控制小时数通过使用线性回归模型来演示，该模型有助于指导控制器在不需要实际数据测量的情况下打开/关闭系统。获得的一个主要成果是能够优化地源热泵的性能，节省一次投入能量和运行周期。此外，调查结果还显示，一年中有近三分之一的时间处于关机节电模式(33%)，而在开机模式下，这一比例为67%。这种智能大数据控制将在生命周期内节省27,020美元。与其他方案相比，这种人工智能节省策略具有竞争性和引领性。值得推荐的是，将GHP控制器与实时雷达或气象站连接起来，为系统提供实时数据条件，从而提高系统的性能，并省去昂贵的测量传感器。
Nowadays sustainable underground geothermal energy resources have received special attention thanks for being characterized as clean, zerocarbon 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 realtime 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.