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Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey

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  • Işık, Erdem
  • Inallı, Mustafa

Abstract

Limited energy resources and increasing need for energy due to population growth seem to lead researchers to focus on these issues. Forecast of meteorological data has significant importance in design of thermal systems. In this study, forecasting of meteorological data used in thermal system design was performed for fifty cities to represent the entire Turkey. Data obtained from General Directorate of Meteorology (MGM) were modelled by artificial neural networks and adaptive-network based fuzzy inference systems. Matlab software was used for modeling and forecasting of prospective data with high sensitivity in thermal systems. Surfer and ArcGIS software were used to create humidity, temperature, solar radiation maps for Turkey. Root mean square error (RMSE), mean absolute error and (MAE), coefficient of variation (COV) and the coefficient of determination (R2) were used to validate the result of the proposed approaches. The results were satisfactory with respect to RMSE, MAE, COV and R2 to forecast the meteorological data. Annual solar power potential maps for Turkey were also proposed and compared with MGM results. The results of the proposed approaches are compatible with the result of the MGM. The Turkey policy maker(s) from MGM can easily use these approaches if a software is constructed.

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  • Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    • Handle: RePEc:eee:energy:v:154:y:2018:i:c:p:7-16
    DOI: 10.1016/j.energy.2018.04.069
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    4. Kong, Xiangfei & Du, Xinyu & Xue, Guixiang & Xu, Zhijie, 2023. "Multi-step short-term solar radiation prediction based on empirical mode decomposition and gated recurrent unit optimized via an attention mechanism," Energy, Elsevier, vol. 282(C).

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