Heating and cooling potential of an earth-to-air heat exchanger using artificial neural network

In this article, we use the concept of artificial neural network and goal oriented design to propose a computer design tool that can help the designer to evaluate any aspect of earth-to-air heat exchanger and behavior of the final configuration. The present study focuses mostly on those aspects related to the passive heating or cooling performance of the building. Two models have been developed for this purpose, namely deterministic and intelligent. The deterministic model is developed by analyzing simultaneously coupled heat and mass transfer in ground whereas the intelligent model is a development of data driven artificial neural network model. Six variables influencing the thermal performance of the earth-to-air heat exchangers which were taken into account are length, humidity, ambient air temperature, ground surface temperature, ground temperature at burial depth and air mass flow rate. Furthermore, a sensitivity analysis was carried out in order to evaluate the impact of various factors involved in the energy balance equation at the burial depth. The model was validated against experimental data sets. Moreover, the developed algorithm is suitable for the calculation of the outlet air temperature and therefore of the heating and cooling potential of the earth-to-air heat exchanger system. The Intelligent model predicts earth-to-air heat exchanger outlet air temperature with an accuracy of ±2.6%, whereas, the deterministic model shows an accuracy of ±5.3%.     

Artificial neural network estimation of global solar radiation using air temperature and relative humidity

Measured air temperature and relative humidity values between 1998 and 2002 for Abha city in Saudi Arabia were used for the estimation of global solar radiation (GSR) in future time domain using artificial neural network method. The estimations of GSR were made using three combinations of data sets namely: (i) day of the year and daily maximum air temperature as inputs and GSR as output, (ii) day of the year and daily mean air temperature as inputs and GSR as output and (iii) time day of the year, daily mean air temperature and relative humidity as inputs and GSR as output. The measured data between 1998 and 2001 were used for training the neural networks while the remaining 240 days’ data from 2002 as testing data. The testing data were not used in training the neural networks. Obtained results show that neural networks are well capable of estimating GSR from temperature and relative humidity. This can be used for estimating GSR for locations where only temperature and humidity data are available.     

The influence of different ground covers on the heating potential of earth-to-air heat exchangers

Ten years' hourly measurements of air and ground temperature values at various depths below bare and short grass soil at Dublin Airport have been used in order to investigate the impact of different ground surface boundary conditions on the efficiency of a single and a multiple parallel earth-to-air heat exchanger system. The heating potential of both these systems buried under bare soil has been assessed and compared with the heating potential of the same systems buried under short-grass-covered soil. The results of this comparison revealed that soil surface cover might be a significant controllable factor for the improvement of the performance of earth-to-air heat exchangers. The heating system consists of a single pipe or multiple parallel pipes laid horizontally, through which ambient or indoor air is propelled and heated by the bulk temperature of the natural ground. The dynamic thermal performance of these systems during the winter period and their operational limits have been calculated using an accurate numerical model. Finally, a sensitivity analysis was performed in order to investigate the effect of the main design parameters, such as pipe length, pipe radius, air velocity inside the tube and the depth of the buried pipe below the earth's surface, on the system heating capacity. Cumulative frequency distributions of the air temperature at the pipe's exit have been developed as a function of the main input parameters.     

Technical note Bin weather data of Athens, Greece

The necessary analysis of the ambient dry bulb temperature and of the relative humidity for elaboration of bin weather data is discussed. Using weather data from Athens, Greece, the annual total bin data as well as monthly bin data in four-hour periods are calculated and presented in tabular form. The presented data serve for the estimation of the energy requirements and fuel consumption of heating and air conditioning systems for either short or long time periods of operation.     

On the impact of urban climate on the energy consumption of buildings

Climatic measurements from almost 30 urban and suburban stations as well as specific measurements performed in 10 urban canyons in Athens, Greece, have been used to assess the impact of the urban climate on the energy consumption of buildings. It is found that for the city of Athens, where the mean heat island intensity exceeds 10°C, the cooling load of urban buildings may be doubled, the peak electricity load for cooling purposes may be tripled especially for higher set point temperatures, while the minimum COP value of air conditioners may be decreased up to 25% because of the higher ambient temperatures. During the winter period, the heating load of centrally located urban buildings is found to be reduced up to 30%. Regarding the potential of natural ventilation techniques when applied to buildings located in urban canyons, it is found that, mainly during the day, this is seriously reduced because of the important decrease of the wind speed inside the canyon. Air flow reduction may be up to 10 times the flow that corresponds to undisturbed ambient wind conditions.     

Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment

Global solar radiation (GSR) data are desirable for many areas of research and applications in various engineering fields. However, GSR is not as readily available as air temperature data. Artificial neural networks (ANNs) are effective tools to model nonlinear systems and require fewer inputs. The objective of this study was to test an artificial neural network (ANN) for estimating the global solar radiation (GSR) as a function of air temperature data in a semi-arid environment. The ANNs (multilayer perceptron type) were trained to estimate GSR as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1994–2001) of daily climatic data collected in weather station of Ahwaz located in Khuzestan plain in the southwest of Iran. The empirical Hargreaves and Samani equation (HS) is also considered for the comparison. The HS equation calibrated by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated HS method. The study demonstrated that modelling of daily GSR through the use of the ANN technique gave better estimates than the HS equation. RMSE and R² for the comparison between observed and estimated GSR for the tested data using the proposed ANN model are 2.534 MJ m^?2 day^?1 and 0.889 respectively.     

Fuzzy model representation of thermosyphon solar water heating system

The aim of this paper is to focus on improvement in prediction accuracy of model for thermosyphon solar water heating (SWH) system. The work employs grey-box modeling approach based on fuzzy system to predict the outlet water temperature of the said system. The prediction performance results are compared with neural network technique, which has been suggested by various researchers in the last one decade. The outlet water temperature prediction by fuzzy modeling technique is analyzed by using 3 models, one with three inputs (inlet water temperature, ambient temperature, solar irradiance), next with two inputs (inlet water temperature, solar irradiance) and last one with single input (solar irradiance/inlet water temperature). An improved prediction performance is observed with three inputs fuzzy model.     

Influence of ambient temperatures on performance of a CO2 heat pump water heating system

In residential applications, an air-to-water CO₂ heat pump is used in combination with a domestic hot water storage tank, and the performance of this system is affected significantly not only by instantaneous ambient air and city water temperatures but also by hourly changes of domestic hot water consumption and temperature distribution in the storage tank. In this paper, the performance of a CO₂ heat pump water heating system is analyzed by numerical simulation. A simulation model is created based on thermodynamic equations, and the values of model parameters are estimated based on measured data for existing devices. The calculated performance is compared with the measured one, and the simulation model is validated. The system performance is clarified in consideration of seasonal changes of ambient air and city water temperatures.     

The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

The main objective of present study is to predict daily global solar radiation (GSR) on a horizontal surface, based on meteorological variables, using different artificial neural network (ANN) techniques. Daily mean air temperature, relative humidity, sunshine hours, evaporation, and wind speed values between 2002 and 2006 for Dezful city in Iran (32°16′N, 48°25′E), are used in this study. In order to consider the effect of each meteorological variable on daily GSR prediction, six following combinations of input variables are considered:

(I)

Day of the year, daily mean air temperature and relative humidity as inputs and daily GSR as output.

(II)

Day of the year, daily mean air temperature and sunshine hours as inputs and daily GSR as output.

(III)

Day of the year, daily mean air temperature, relative humidity and sunshine hours as inputs and daily GSR as output.

(IV)

Day of the year, daily mean air temperature, relative humidity, sunshine hours and evaporation as inputs and daily GSR as output.

(V)

Day of the year, daily mean air temperature, relative humidity, sunshine hours and wind speed as inputs and daily GSR as output.

(VI)

Day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed as inputs and daily GSR as output.

Multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are applied for daily GSR modeling based on six proposed combinations.The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data.The comparison of obtained results from ANNs and different conventional GSR prediction (CGSRP) models shows very good improvements (i.e. the predicted values of best ANN model (MLP-V) has a mean absolute percentage error (MAPE) about 5.21% versus 10.02% for best CGSRP model (CGSRP 5)).     

Neural network approach for food temperature prediction during solar drying

In the present study, the application of artificial neural network (ANN) for prediction of temperature variation of food product during solar drying is investigated. The important climatic variables namely, solar radiation intensity and ambient air temperature are considered as the input parameters for ANN modeling. Experimental data on potato cylinders and slices obtained with mixed mode solar dryer for 9 typical days of different months of the year were used for training and testing the neural network. A methodology is proposed for development of optimal neural network. Results of analysis reveal that the network with 4 neurons and logsig transfer function and trainrp back propagation algorithm is the most appropriate approach for both potato cylinders and slices based on minimum measures of error. In order to test the worthiness of ANN model for prediction of food temperature variation, the analytical heat diffusion model with appropriate boundary conditions and statistical model are also proposed. Based on error analysis results, the prediction capability of ANN model is found to be the best of all the prediction models investigated, irrespective of food sample geometry.     

The importance of the stochastic component of climatic variables in simulating the thermal behaviour of domestic dwellings

The determination of seasonal heating or cooling loads in domestic dwellings would be greatly simplified if the forcing functions of the dwellings, the climatic variables, could be represented using only the steady periodic or deterministic component. The differential equations in the time domain describing heat transfer processes could be transformed to algebraic equations in the frequency domain, which could be easily solved through matrix inversion. Temperatures or loads could then be found through transformation back to the time domain. It has been demonstrated that, for solar process heat systems, the stochastic or non-deterministic component of the ambient temperature can be ignored without greatly affecting energy determinations, although the stochastic component of the solar radiation has to be included to give realistic results. In this paper it is shown that the stochastic component is critical for both solar radiation and ambient temperature when determining heating and cooling loads for domestic dwellings.     

Thermal management of solar photovoltaics modules for enhanced power generation

Industry and government interest in solar energy has increased in recent years in the Middle East. However, despite high levels of solar irradiance in the Arabian Gulf, harsh climatic conditions adversely affect the electrical performance of solar photovoltaics (PV). The objective of this study is to compare the annual performance characteristics of solar PV modules that utilize either sun-tracking or water cooling to increase electrical power generation relative to that of stationary, passively cooled modules in the Middle East climatic conditions. This is achieved using an electro-thermal model developed and validated against experimental data acquired in this study. The model is used to predict the annual electrical power output of a 140 W PV module in Abu Dhabi (24.43°N, 54.45°E) under four operating conditions: (i) stationary geographical south facing orientation with passive air cooling, (ii) sun-tracked orientation with passive air cooling, (iii) stationary geographical south facing orientation with water cooling at ambient air temperature, and (iv) stationary geographical south facing orientation with water refrigerated at either 10 °C or 20 °C below ambient air temperature. For water cooled modules, annual electrical power output increases by 22% for water at ambient air temperature, and by 28% and 31% for water refrigerated at 10 °C and 20 °C below ambient air temperature, respectively. 80% of the annual output enhancement obtained using water cooling occurs between the months of May and October. Finally, whereas the annual yield enhancement obtained with water cooling at ambient air temperature from May to October is of 18% relative to stationary passive cooling conditions, sun-tracking over the complete year produces an enhancement of only 15% relative to stationary passive cooling conditions.     

ON THE USE OF DETERMINISTIC AND INTELLIGENT TECHNIQUES TO PREDICT THE AIR VELOCITY DISTRIBUTION ON EXTERNAL OPENINGS IN SINGLE-SIDED NATURAL VENTILATION CONFIGURATIONS

Four single-sided natural ventilation experiments were carried out in a test cell, a full-scale outdoor facility in Athens, Greece. During the experiments air velocity data at various heights at the vertical centerline of the opening were collected. Various mathematical approaches were used for the prediction of the air velocity at the door level, using experimentally derived parameters as inputs. Deterministic as well as intelligent techniques were applied in an attempt to determine the potential of each of these approaches in predicting the air velocity with accuracy. In the frame of the first category, correlation techniques, pressure models and CFD models were studied. The intelligent techniques involved an application of fuzzy theory for the prediction of the air velocity at the opening level. The response of each of the above methodologies is discussed in this paper.     

基于神经网络的电站锅炉空气预热器积灰在线监测模型研究

针对大型电站锅炉空气预热器受热面积灰状况进行了分析研究。应用3层神经网络构建了300MW电站锅炉空气预热器受热面积灰监测数学模型,选择锅炉负荷、烟气差压、排烟温度等参数作为输入向量,以反映空气预热器积灰状况的污染系数作为输出向量,利用电厂DCS系统采集的机组实时数据,经规格化处理后作为样本集对网络进行训练。训练过程中,通过添加动量项和变步长改进了BP算法。将建立的模型应用于华电国际青岛发电公司#2炉的空气预热器在线积灰监测,取得了较好的结果。     

Analysis of the summer ambient temperatures for cooling purposes

The necessary analysis of the summer ambient temperature data for evaluation of passive and hybrid cooling techniques and components is discussed. A complete analysis using data from Athens, Greece, is presented. Daily, monthly, and annual distributions of the more important parameters, as well as statistical indexes, are estimated and discussed. Correlation techniques are used, and expressions to predict accurately the necessary parameters are proposed. The presented analysis aims to define an appropriate format to report on the summer ambient temperature data used for cooling purposes.     

Solar heat pump systems for domestic hot water

Vapour compression heat pumps can upgrade ambient heat sources to match the desired heating load temperature. They can offer considerable increase in operational energy efficiency compared to current water heating systems. Solar heat pumps collect energy not only from solar radiation but also from the ambient air. They can operate even at night or in totally overcast conditions. Since the evaporator/collector operates at temperatures lower than ambient air temperature it does not need glazing or a selective coating to prevent losses. Currently, however, they are not used much at all in domestic or commercial water heating systems. In this paper comparison is made of a conventional solar hot water system, a conventional air source heat pump hot water system and a solar heat pump water heating system based on various capital city locations in Australia. A summary is given of specific electricity consumption, initial and operating costs, and greenhouse gas generation of the three systems dealt with in this paper. The ultimate choice of unit for a particular location will depend heavily on the solar radiation, climate and the local price paid for electricity to drive or boost the unit chosen.     

Estimation of ambient temperature bin data from monthly average temperatures and solar clearness index. Validation of the methodology in two Greek cities

Ambient temperature bin data are used for estimating the energy consumption in HVAC systems with air-source heat pumps and cooling equipment. In this paper a methodology for estimating the ambient temperature bin data, based on monthly average outdoor temperatures and solar clearness index, is presented. For the two most populated cities of Greece, namely Athens and Thessaloniki, the estimated data are compared to the bin data produced by statistical analysis of 10 years hourly dry-bulb temperature measurements. Both data sets were also used for estimating the heating and cooling energy requirements of a case study building. The results obtained are similar, with very small differences, suggesting that the proposed methodology can be used for estimating bin data for other cities.     

Heating and cooling degree-hours for Athens and Thessaloniki, Greece

The purpose of this study is to determine and present heating and cooling degree-hours for the two main cities in Greece, namely Athens and Thessaloniki, using hourly dry bulb temperature records from the meteorological stations of the National Observatory of Athens and of the Aristotle University of Thessaloniki. The heating degree-hours were calculated for base temperatures from 10 to 20 °C and the cooling degree-hours for base temperatures from 20 to 27.5 °C, using a temperature step of 0.5 °C. The results are presented in tabular form and serve the estimation of the energy requirements and fuel consumption of heating and air conditioning systems for either monthly or seasonally operation.     

Thermal modeling of a greenhouse integrated to an aquifer coupled cavity flow heat exchanger system

A thermal model is developed for heating and cooling of an agricultural greenhouse integrated with an aquifer coupled cavity flow heat exchanger system (ACCFHES). The ACCFHES works on the principal of utilizing deep aquifer water available at the ground surface through an irrigation tube well already installed in every agricultural field at constant year-round temperature of 24 °C. The analysis is based on the energy balance equations for different components of the greenhouse. Using the derived analytical expressions, a computer program is developed in C⁺⁺ for computing the hourly greenhouse plant and room air temperature for various design and climatic parameters. Experimental validation of the developed model is carried out using the measured plant and room air temperature data of the greenhouse (in which capsicum is grown) for the winter and summer conditions of the year 2004–2005 at Chandigarh (31°N and 78°E), Punjab, India. It is observed that the predicted and measured values are in close agreement. Greenhouse room air and plant temperature is maintained 6–7 K and 5–6 K below ambient, respectively for an extreme summer day and 7–8 K and 5–6 K above ambient, respectively for an extreme winter night. Finally, parametric studies are conducted to observe the effect of various operating parameters such as mass of the plant, area of the plant, mass flow rate of the circulating air and area of the ACCFHES on the greenhouse room air and plant temperature.     

Parametric and experimental study on thermal performance of an earth–air heat exchanger

In the present paper a quasi‐steady state mathematical model is developed to predict the outlet air temperature and monthly heating and cooling potentials of an earth–air heat exchanger. Monthly values of heating and cooling potentials are estimated by rigorous experimentation throughout the year for composite climate of New Delhi. The uncertainty values are calculated for each month; for December the value is 4.9%. It is observed that there is an 8.9 and a 5.9°C temperature rise and fall during winter and summer due to the earth–air heat exchanger buried at a depth of 1.5 m underground. The correlation coefficient, root mean square of percentage deviation, reduced chi‐square and mean bias error have been computed for each month. The values are 1, 3.0%, 0.8 and ?0.63 for December. Statistical analysis shows that there is fair agreement between theoretical results and experimental observations for each month. Monthly values of heating and cooling potentials have also been predicted for other climatic conditions in India namely Jodhpur, Chennai, Mumbai and Kolkata. Copyright © 2005 John Wiley & Sons, Ltd.