Offshore wind power development in Europe and its comparison with onshore counterpart

Wind power, as a renewable source of energy, produces no emissions and is an excellent alternative in environmental terms to conventional electricity production based on fuels such as oil, coal or natural gas. At present, the vast majority of wind power is generated from onshore wind farms. However, their growth is limited by the lack of inexpensive land near major population centers and the visual pollution caused by large wind turbines. Comparing with onshore wind power, offshore winds tend to flow at higher speeds than onshore winds, thus it allows turbines to produce more electricity. Estimates predict a huge increase in wind energy development over the next 20 years. Much of this development will be offshore wind energy. This implies that great investment will be done in offshore wind farms over the next decades. For this reason, offshore wind farms promise to become an important source of energy in the near future. In this study, history, current status, investment cost, employment, industry and installation of offshore wind energy in Europe are investigated in detail, and also compared to its onshore counterpart. © 2010 Elsevier Ltd. All rights reserved

Water Demand Forecasting Based on Stepwise Multiple Nonlinear Regression Analysis

The main objective of the present study is to apply the nonlinear regression (NLR) model in order to forecast water demand in Adana city of Turkey. The average monthly water bill, total subscribership, atmospheric temperature, relative humidity, rainfall, global solar radiation, sunshine duration, wind speed and atmospheric pressure are selected as independent variables. Meteorological parameters were taken from Adana meteorological station, and the other parameters such as water consumption, total subscribership and water bill values were supplied from Adana Water and Sewerage Administration during the periods of 2000-2009. In order to get a successful simulation, first, all independent variables were added to the "enter" regression model. Then, the method of stepwise multiple regression was applied for the selection of the "best" regression equation (model). Thus, the best independent variables were selected for the NLR model. Consequently, while water consumption in Adana city is 3. 84 million m3 at the end of 2009, it will increase up to 4. 99 million m3 by the year 2020. © 2012 King Fahd University of Petroleum and Minerals

Experimental implementation of a split-type air conditioner for fault detection and diagnosis

The use of split-type air conditioners (STAC) is very popular in residential and commercial buildings and demand for these types of air conditioners is increasing year by year. Theoretical and practical case studies for fault detection and diagnosis of STAC constitute a vital part in technical services and refrigeration/air-conditioning laboratories of mechanical engineering. Fast detection of fault is quite important for technical services. In this study, a fault-test prototype of split-type air conditioner has been designed and implemented. In order to perform the performance analysis, sixteen different faults have been created on the control unit of this prototype. The behavior of the system has been also investigated under the normal and faulty conditions. The test results for faults are presented and compared with the normal operating results. In addition, solutions offered against the faults have been also provided for the participants. © Sila Science

Performance evaluation of a split air conditioning system with artificial neural network approach

This paper deals with predicting the performance of a split air conditioning (SAC) system using artificial neural network (ANN) approach. For this aim, an experimental R-22 split air conditioning system was developed and equipped with instruments used for temperature, pressure, current and power measurements. The experimental system was operated at steady state conditions varying the condenser inlet air temperature. Using some of the experimental data for training, an ANN model for the SAC system was developed. Inputs of the ANN model include the condenser inlet air temperature and evaporating temperature. Outputs of the ANN model consist of the ideal cooling and heating coefficients of performance, cooling and heating coefficients of performance, compressor isentropic efficiency, compressor power, cooling capacity, heat rejection rate in the condenser, refrigerant mass flow rate, evaporator inlet and outlet air temperatures, condenser outlet air temperature, condensing temperature and compressor current. The ANN predictions for these parameters usually agreed well with the experimental values with mean relative errors (MREs) in the range of 0.03-4.55%, root mean square errors (RMSEs) in the range of 0.0071-0.7573, and absolute fraction of variance (R2) in the range of 0.99798-1.00. This study shows that SAC systems can be alternatively be modeled using ANNs with a high degree of accuracy. © Sila Science

Electric energy demands of Turkey in residential and industrial sectors

The main objective of the present study is to apply the artificial neural network (ANN) methodology, linear regression (LR) and nonlinear regression (NLR) models to estimate the electricity consumptions of the residential and industrial sectors in Turkey. Installed capacity, gross electricity generation, population and total subscribership were selected as independent variables. Two different scenarios (powerful and poor) were proposed for prediction of the future electricity consumption. Obtained results of the LR, NLR and ANN models were also compared with each other as well as the projection of the Ministry of Energy and Natural Resources (MENR) and the results in literature. Results of the comparison showed that the performance values of the ANN method are better than the performance values of the LR and NLR models. According to the poor scenario and ANN model, Turkey's residential and industrial sector electricity consumptions will increase to value of 140.64 TWh and 124.85 TWh by 2015, respectively. © 2011 Elsevier Ltd. All rights reserved

Estimation of relative humidity based on artificial neural network approach in the Aegean Region of Turkey

The aim of this study is to estimate the monthly mean relative humidity (MRH) values in the Aegean Region of Turkey with the help of the topographical and meteorological parameters based on artificial neural network (ANN) approach. The monthly MRH values were calculated from the measurement in the meteorological observing stations established in Izmir, Mugla, Aydin, Denizli, Usak, Manisa, Kutahya and Afyonkarahisar provinces between 2000 and 2006. Latitude, longitude, altitude, precipitation and months of the year were used in the input layer of the ANN network, while the MRH was used in output layer of the network. The ANN model was developed using MATLAB software, and then actual values were compared with those obtained by ANN and multi-linear regression methods. It seemed that the obtained values were in the acceptable error limits. It is concluded that the determination of relative humidity values is possible at any target point of the region where the measurement cannot be performed. © 2011 Springer-Verlag

Estimation of human heat loss in five Mediterranean regions

PubMedID: 26025784This study investigates the effects of seasonal weather differences on the human body's heat losses in the Mediterranean region of Turkey. The provinces of Adana, Antakya, Osmaniye, Mersin and Antalya were chosen for the research, and monthly atmospheric temperatures, relative humidity, wind speed and atmospheric pressure data from 2007 were used. In all these provinces, radiative, convective and evaporative heat losses from the human body based on skin surface and respiration were analyzed from meteorological data by using the heat balance equation. According to the results, the rate of radiative, convective and evaporative heat losses from the human body varies considerably from season to season. In all the provinces, 90% of heat loss was caused by heat transfer from the skin, with the remaining 10% taking place through respiration. Furthermore, radiative and convective heat loss through the skin reached the highest values in the winter months at approximately between 110 and 140W/m2, with the lowest values coming in the summer months at roughly 30-50W/m2. © 2015 Elsevier Inc

Effect of atmospheric temperature on exergy efficiency and destruction of a typical residential split air conditioning system

This work presents an experimental investigation on energy and exergy performance characteristics of a typical residential split air conditioning system. Analyses are performed for different atmospheric temperatures. On the basis of experimental data coefficient of performances (COP), exergy efficiencies (?) and exergy destructions (Exdest) of whole system and its each subunits are evaluated. The results also demonstrate that the atmospheric temperature (Tatm) rise affects the system performance as well as its performance of components substantially. The values of Exdest in the compressor and capillary tube increase with an increase of Tatm but the values of Exdest in the condenser and evaporator decrease with an increase of Tatm. When Tatm is risen from 20°C to 46°C; it is determined that power consumption comp (Wcomp) and Exdest of the air conditioning system are enhanced by 47.1% and 24.5%, respectively, but ?is reduced by 38.8%. © 2016 Inderscience Enterprises Ltd

Investigation of wind power density at different heights in the Gelibolu peninsula of Turkey

This study aims to determine the wind characteristics and wind power potential of the Gelibolu peninsula in the Çanakkale region of Turkey. For this purpose, hourly average wind data observed at the Gelibolu meteorological station were used. The Weibull probability density functions and Weibull parameters of time-series of wind speed, mean wind speed, and mean wind power potential were determined for different heights as 10, 20, 30, 40, and 50 m. According to the results obtained at 10- and 50-m heights above the ground level, the annual wind speed varied from 6.85 to 8.58 m/s in this region, respectively. The annual wind power potential of the site was determined as 407 and 800 W/m2 for 10- and 50-m heights, respectively. These results indicate that the investigated site has a reasonable wind power potential for generating electricity. © 2016 Taylor & Francis