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Abstract: Energy consumption in buildings is heavily affected by climate change. It causes significant variations of the outdoor design conditions, cooling and heating loads. In this paper, a total of 40 years (1967–2006) of hourly temperature data were considered in four decades and outdoor design conditions for each decade were calculated. It was found that wet-bulb temperature (WBT) has increased by 3.28°C during the mentioned period, which impacts on design and selection of the cooling equipment. The results show that cooling hours is projected to increase around 15% over the whole 1967–2006 period. The most important result found in this study is the inability of direct evaporative cooling (DEC) systems in preparing comfort condition after 1997, whereas these systems provided thermal comfort in Tehran''s buildings from 1967 to 1996. The prediction of climate change impacts on the outdoor design conditions and cooling systems performance until 2020 demonstrates dry bulb temperature (DBT) and WBT increase 1.05°C and 2.42°C respectively. Hence, indirect–direct evaporative cooling systems (IDEC) may not be responsible for the years after 2016. Besides, the additional energy for absorption and vapor compression (VC) cooling systems will grow quickly in the future. [Copyright &y& Elsevier]

Abstract: The well-known fuzzy rule-based Takagi–Sugeno–Kang (TSK) model is combined with a set of fuzzy regressions (FR) to investigate the impact of the climate change on the electricity consumption duration. The electricity demand forecasts in the short-terms have a vital application in electricity markets. Knowing that the energy is a product of the relation between the climate change and the average consumption duration of the peak load. The paper introduces a type III TSK fuzzy inference machine combined with a set of linear and nonlinear fuzzy regressors in the consequent part to model effects of the climate change on the electricity demand. However, a simplified version of the model is applied to daily data of the average temperature in Tehran, 2004. First, based on an initially fitted nonlinear curve, an optimization model is employed to cluster data into three groups of cold, temperate and hot. The fuzzy data have been expanded to reduce the temperature volatile property. Then the relation is estimated by the fuzzy regressions in company with the TSK model. Numerical results show high efficiency of the proposed combined fuzzy model, as well as a minor decrease in the average absolute error. [Copyright &y& Elsevier]