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This paper aims to improve the spatial accuracy of urban growth simulation models and clarify any associated uncertainties. Artificial Neural Networks (ANNs), Random Forest (RF), and Logistic Regression (LR) were implemented to simulate urban growth in the megacity of Tehran, Iran, as a case study. Model calibration was performed using data between 1985 and 1999 whereas the data between 1999 and 2014 was used for model validation. First of all, Transition Index Maps (TIMs) were computed by means of each model to assess the potential of urban growth for each cell. Using the standard deviation, consensus between the TIMs was evaluated. Because the TIMs of the individual models manifested discrepancies, they were combined using a number of ensemble forecasting approaches including median, mathematical average, principle component analysis, and weighted area under the total operating characteristic. The individual and combined TIMs were then put into Cellular Automata (CA) to simulate the future pattern of urban growth in Tehran. The results were evaluated in two stages. At first, the TIMs were evaluated by means of Total Operating Characteristics (TOC), and then a set of statistical indices was used to evaluate the spatial accuracy of the simulated urban growth maps. The best result was obtained by median ensemble forecasting, whereas the LR model showed the lowest level of accuracy. In similar studies, it is recommended to implement and compare different ensemble methods when integrating individual models. • We compared the effectiveness of ANNs, RF and LR for urban growth modelling. • Standard error map indicated discrepancy among the results of individual models. • Various ensemble forecasting approaches were proposed to combine individual models. • Mean ensemble forecasting created more accurate simulated maps. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient methodology for the simulation-based multi-objective optimization problems, which addresses important limitations for the optimization of the building energy performance. In this work, a mono- and multi-objective particle swarm optimization (MOPSO) algorithm is coupled with EnergyPlus building energy simulation software to find a set of non-dominated solutions to enhance the building energy performance. To evaluate the capability and effectiveness of the approach, the developed method is applied to a single room model, and the effect of building architectural parameters including, the building orientation, the shading overhang specifications, the window size, and the glazing and the wall material properties on the building energy consumption are studied in four major climatic regions of Iran. In the optimization section, mono-criterion and multi-criteria optimization analyses of the annual cooling, heating, and lighting electricity consumption are examined to understand interactions between the objective functions and to minimize the annual total building energy demand. The achieved optimum solutions from the multi-objective optimization process are also reported as Pareto optimal fronts. Finally, the result of multi-criteria minimization is compared with the mono-criterion ones. The results of the triple-objective optimization problem point out that for our typical model, the annual cooling electricity decreases about 19.8–33.3%; while the annual heating and lighting ones increase 1.7–4.8% and 0.5–2.6%, respectively, in comparison to the baseline model for four diverse climatic regions of Iran. In addition, the optimum design leads to 1.6–11.3% diminution of the total annual building electricity demand. The proposed optimization method shows a powerful and useful tool that can save time while searching for the optimal solutions with conflicting objective functions; therefore facilitate decision making in early phases of a building design in order to enhance its energy efficiency. [ABSTRACT FROM AUTHOR]

This paper assess the feasibility of using heating-cooling radiant panel in residential buildings in five different climate zones in Iran. This study leverages simulation modeling to evaluate the thermal performance of heating-cooling radiant panels. By defining new indices, applicability limiting factors of the radiant panels, i.e. condensation risk in cooling, and asymmetric radiation discomfort in heating are evaluated and compared in different climatic conditions. The system capacity and setpoints are calculated based on thermal comfort conditions, using PMV index in heating (PMV= -0.5) and cooling (PMV= +0.5) modes. Results of this study indicate that for dryer regions with less risk of condensation the radiant system can help home owners to save around 11.3% on their heating energy and 9.1% on cooling energy use. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy systems, combining various kinds of technologies, have shown relatively high capabilities to solve reliability problems and have reduced cost challenges. The use of hybrid electricity generation/storage technologies as off-grid stand-alone systems is reasonable to overcome related shortcomings. Solar and wind energy are two rapidly emerging renewable ones that have precedence in comparison to the other kinds. In this regard, the present paper studies four specific locations in Iran, which are candidates for research centers. Based on the solar radiation and average wind speed maps, techno-economically optimized systems are designed by simulating behavior of various combinations of renewable energy systems with different sizing, including wind turbine (WT), photovoltaic (PV), fuel cell (FC), and battery banks. According to the results obtained by a computer program, it is concluded that the hybrid systems including WT and PV with battery backup are less costly compared to the other systems. Moreover, we found that among non-hybrid systems, in most regions of Iran׳s territory PVs are more economical than WTs. Despite of its advantages, FC has not been applied in the optimal systems due to its high initial cost and its low replacement life. [ABSTRACT FROM AUTHOR]