scholarly journals Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Hao Li ◽  
Zhijian Liu ◽  
Kejun Liu ◽  
Zhien Zhang
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Potential Application ◽  
High Throughput Screening ◽  
High Performance ◽  
Predictive Power ◽  
Prediction Method ◽  
Solar Water Heater ◽  
Water Heater ◽  
Solar Water

Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

scholarly journals Evaluation of a High-Performance Solar Home in Loveland, Colorado

2006 ◽  
Vol 129 (2) ◽  
pp. 226-234
Author(s):  
Robert Hendron ◽  
Mark Eastment ◽  
Ed Hancock ◽  
Greg Barker ◽  
Paul Reeves
Keyword(s):  
High Performance ◽  
Energy Savings ◽  
High Efficiency ◽  
Building Envelope ◽  
Operating Conditions ◽  
Hot Water ◽  
Space Heating ◽  
Solar Water Heater ◽  
Water Heater ◽  
Solar Water

Building America (BA) partner McStain Neighborhoods built the Discovery House in Loveland, CO, with an extensive package of energy-efficient features, including a high-performance envelope, efficient mechanical systems, a solar water heater integrated with the space-heating system, a heat-recovery ventilator (HRV), and ENERGY STAR appliances. The National Renewable Energy Laboratory (NREL) and Building Science Consortium conducted short-term field-testing and building energy simulations to evaluate the performance of the house. These evaluations are utilized by BA to improve future prototype designs and to identify critical research needs. The Discovery House building envelope and ducts were very tight under normal operating conditions. The HRV provided fresh air at a rate of about 35L∕s(75cfm), consistent with the recommendations of ASHRAE Standard 62.2. The solar hot water system is expected to meet the bulk of the domestic hot water (DHW) load (>83%), but only about 12% of the space-heating load. DOE-2.2 simulations predict whole-house source energy savings of 54% compared to the BA Benchmark (Hendron, R., 2005 NREL Report No. 37529, NREL, Golden, CO). The largest contributors to energy savings beyond McStain’s standard practice are the solar water heater, HRV, improved air distribution, high-efficiency boiler, and compact fluorescent lighting package.

scholarly journals Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

2018 ◽  
pp. 55-74 ◽  
Author(s):  
Hao Li ◽  
Zhijian Liu
Keyword(s):  
Machine Learning ◽  
Machine Learning Method ◽  
Solar Water Heater ◽  
Learning Method ◽  
Optimization Strategy ◽  
Learning Models ◽  
Water Heater ◽  
Solar Water ◽  
Knowledge Based ◽  
Machine Learning Models

Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost, and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately, a knowledge-based machine learning method can provide a promising prediction and optimization strategy for the performance of energy systems. In this chapter, the authors show how they utilize the machine learning models trained from a large experimental database to perform precise prediction and optimization on a solar water heater (SWH) system. A new energy system optimization strategy based on a high-throughput screening (HTS) process is proposed. This chapter consists of: 1) comparative studies on varieties of machine learning models (artificial neural networks [ANNs], support vector machine [SVM], and extreme learning machine [ELM]) to predict the performances of SWHs; 2) development of an ANN-based software to assist the quick prediction; and 3) introduction of a computational HTS method to design a high-performance SWH system.

scholarly journals Evaluation of a High-Performance Solar Home in Loveland, Colorado

Solar Energy ◽  
2005 ◽  
Author(s):  
Robert Hendron ◽  
Mark Eastment ◽  
Ed Hancock ◽  
Greg Barker ◽  
Paul Reeves
Keyword(s):  
High Performance ◽  
Energy Savings ◽  
High Efficiency ◽  
Building Envelope ◽  
Operating Conditions ◽  
Hot Water ◽  
Space Heating ◽  
Solar Water Heater ◽  
Water Heater ◽  
Solar Water

Building America (BA) partner McStain Neighborhoods built the Discovery House in Loveland, Colorado, with an extensive package of energy-efficient features, including a high-performance envelope, efficient mechanical systems, a solar water heater integrated with the space-heating system, a heat-recovery ventilator (HRV), and ENERGY STAR™ appliances. The National Renewable Energy Laboratory (NREL) and Building Science Consortium (BSC) conducted short-term field-testing and building energy simulations to evaluate the performance of the house. These evaluations are utilized by BA to improve future prototype designs and to identify critical research needs. The Discovery House building envelope and ducts were very tight under normal operating conditions. The HRV provided fresh air at a rate of about 75 cfm (35 l/s), consistent with the recommendations of ASHRAE Standard 62.2. The solar hot water system is expected to meet the bulk of the domestic hot water (DHW) load (>83%), but only about 12% of the space-heating load. DOE-2.2 simulations predict whole-house source energy savings of 54% compared to the BA Benchmark [1]. The largest contributors to energy savings beyond McStain’s standard practice are the solar water heater, HRV, improved air distribution, high-efficiency boiler, and compact fluorescent lighting package.

Modernization of Water Treatment System at Agricultural Enterprises Due to Introduction of Solar Energy

2020 ◽  
Vol 67 (1) ◽  
pp. 142-147
Author(s):  
Alina A. Aleksandrova ◽  
Maksim S. Zhuzhin ◽  
Yuliya M. Dulepova
Keyword(s):  
Mathematical Model ◽  
Water Treatment ◽  
Solar Energy ◽  
Energy Saving ◽  
Electric Energy ◽  
Solar Water Heater ◽  
Water Heater ◽  
Agricultural Enterprises ◽  
Solar Water ◽  
The Cost

Energy saving today is an integral part of the development strategy of agricultural organizations. Considerable attention is paid to the modernization and automation of technological processes in agricultural enterprises, which can improve the quality of work and reduce the cost of production. The direction of modernization is to reduce the consumption of electric energy by improving the water treatment system in livestock complexes. (Research purpose) The research purpose is to determine the potential of solar energy used in the Nizhny Novgorod region and to determine the possibility of its use for water heating in livestock complexes and to consider the cost-effectiveness of using a device to heat water through solar energy. (Materials and methods) Authors used an improved algorithm of Pixer and Laszlo, applied in the NASA project «Surface meteorology and Energy», which allows to calculate the optimal angle of inclination of the device for heating water. (Results and discussion) Designed a mock-up of a livestock complex with a solar water heater installed on the roof, protected by patent for invention No. 2672656. A mathematical model was designed experimentally to predict the results of the plant operation in non-described modes. (Conclusions) The article reveales the optimal capacity of the circulation pump. Authors have created a mathematical model of the device that allows to predict the water heating in a certain period of time. The article presents the calculations on the energy and economic efficiency of using a solar water heater. An electric energy saving of about 30 percent, in the economic equivalent of 35 percent.

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