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American Society for Testing and Materials

Energy resources and their utilization intimately relate to sustainable development. In attaining sustainable development, increasing the energy efficiencies of processes utilizing sustainable energy resources plays an important role. The utilization of renewable energy offers a wide range of exceptional benefits. There is also a link between exergy and sustainable development. A sustainable energy system may be regarded as a cost-efficient, reliable, and environmentally friendly energy system that effectively utilizes local resources and networks. Exergy analysis has been widely used in the design, simulation and performance evaluation of energy systems. The present study comprehensively reviews exergetic analysis and performance evaluation of a wide range of renewable energy resources (RERs) for the first time to the best of the author's knowledge. In this regard, general relations (i.e., energy, exergy, entropy and exergy balance equations along with exergy efficiency, exergetic improvement potential rate and some thermodynamic parameters, such as fuel depletion ratio, relative irreversibility, productivity lack and exergetic factor) used in the analysis are presented first. Next, exergetically analyzed and evaluated RERs include (a) solar energy systems; (a1) solar collector applications such as solar water heating systems, solar space heating and cooling, solar refrigeration, solar cookers, industrial process heat, solar desalination systems and solar thermal power plants), (a2) photovoltaics (PVs) and (a3) hybrid (PV/thermal) solar collectors, (b) wind energy systems, (c) geothermal energy systems, (c1) direct utilization (district heating, geothermal or ground-source heat pumps, greenhouses and drying) and (c2) indirect utilization (geothermal power plants), (d) biomass, (e) other renewable energy systems, and (f) country based RERs. Studies conducted on these RERs are then compared with the previously ones in tabulated forms, while the Grassmann (or exergy flow) diagrams, which are a very useful representation of exergy flows and losses, for some RERs are given. Finally, the conclusions are presented. It is expected that this comprehensive study will be very beneficial to everyone involved or interested in the exergetic design, simulation, analysis and performance assessment of RERs.

A key review on exergetic analysis and assessment of renewable energy resources for a sustainable future

Computer Aided Civil and Infrastructure Engineering

Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review

Artificial intelligence based models for stream-flow forecasting: 2000–2015

This paper presents a Takagi Sugeno (TS) fuzzy method for predicting future monthly water consumption values from three antecedent water consumption amounts, which are considered as independent variables. Mean square error (MSE) values for different model configurations are obtained, and the most effective model is selected. It is expected that this model will be more extensively used than Markov or ARIMA (AutoRegressive Integrated Moving Average) models commonly available for stochastic modeling and predictions. The TS fuzzy model does not have restrictive assumptions such as the stationarity and ergodicity which are primary requirements for the stochastic modeling. The TS fuzzy model is applied to monthly water consumption fluctuations of Istanbul city in Turkey. In the prediction procedure only lag one is considered. It is observed that the TS fuzzy model preserves the statistical properties. This model also helps to make predictions with less than 10% relative error.

Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach

The water level of Urmia Lake, the largest inland lake in Iran with maximum water surface area of about 6000 km2, has been shrinking for the last two decades. Although a number of study have been performed to determine drought condition and coastline changes of Urmia Lake, there has not been a detailed study to distinguish anthropogenic effects from climate impacts on the drying of Urmia Lake. In this study, water budget of Urmia Lake and the intensity of drought in the basin were analyzed in the period from 1985 to 2010 and a new hypothesis is proposed to quantify anthropogenic and climate impacts in reducing the volume of Urmia Lake. The results of this study indicate that human impacts on the Lake and its basin are more important than climate factors. Though previous studies assumed that ground water output from Urmia Lake is negligible, the results of this study show the presence of significant groundwater seepage from Urmia Lake. Major changes in the variables that reduced the water level of Urmia Lake were observed since 1998. Anthropogenic impacts and climate factors have roughly 80% and 20% effects on the drying up of Urmia Lake, respectively. Hence, the first step to recover Urmia Lake could be the revision of management surface water, operation of dams and groundwater resources. The second step could be the review and classification of agricultural products grown in the region in terms of water consumption and teach local people the best practice methods for irrigation.

Investigation Anthropogenic Impacts and Climate Factors on Drying up of Urmia Lake using Water Budget and Drought Analysis

In practice, rainfall–runoff relationships are achieved through a simply defined runoff coefficient concept that is widely used in many engineering hydrological designs in urban and rural areas. The simplicity of the method, with the sole requirement of runoff coefficient assessment, is the main attractiveness, in addition to its successful prediction of average runoff rates for a given rainfall record. Unfortunately, in the classical regression approach of the rainfall–runoff relationship, internal variabilities are not taken into consideration explicitly. The runoff coefficient is considered a constant value, and it is used without distinction of antecedent conditions for the calculation of runoff from the rainfall record. In this paper, various other uncertainty embedded versions of the runoff coefficient, and hence rainfall–runoff formulation, are presented in terms of statistics, probability, perturbation and, finally, fuzzy system modelling. It is concluded that the fuzzy logic approach yields the least relative error among the various alternative runoff calculation methods; therefore, it is recommended for use in future studies. The application of various alternatives is presented for two monthly rainfall-runoff records around Istanbul, Turkey. Copyright © 2005 John Wiley & Sons, Ltd.

A comparative fuzzy logic approach to runoff coefficient and runoff estimation

A comprehensive hydrological model, like the storm water management model (SWMM), has been widely used for rainfall-runoff simulation. In recent years, simple and effective modern modeling techniques have also brought great attention to the prediction of runoff with rainfall input. A comparative case study between SWMM and a presently developed fuzzy logic model for the predictions of total runoff within the watershed of Cascina Scala, Pavia in Italy is presented. A data set of 23 events from 2000 to 2003 including with the total rainfall and total runoff are adopted to train fuzzy logic parameters. Other data (1990–1995) with detailed time variations of rainfall and runoff are available for the setup and calibration of SWMM for runoff modeling. Among the 1990–1995 data, 35 independent rainfall events are selected to test the prediction performance of the SWMM and fuzzy logic models by comparing the predicted total runoffs with measured data. Comparisons and performance analyses in terms of the root-mean-squared error and coefficient of efficiency are made between the SWMM and the fuzzy logic model. The predicted total runoffs from either the SWMM or the fuzzy logic model are found to agree reasonably well with the measured data. For large rainfall events, the fuzzy logic model generally outperforms the SWMM unless the modification of the impervious ratio is applied to improve the SWMM results. However, the SWMM can produce the time varying hydrograph whereas fuzzy logic is subject to limitation of the methodology and is unable to generate such an output.

Abdüsselam Altunkaynak

Papers

Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach

Investigation Anthropogenic Impacts and Climate Factors on Drying up of Urmia Lake using Water Budget and Drought Analysis

A comparative fuzzy logic approach to runoff coefficient and runoff estimation

Comparative Case Study of Rainfall-Runoff Modeling between SWMM and Fuzzy Logic Approach

Sediment load prediction by genetic algorithms