Estimation of renewable energy and built environment-related variables using neural networks - A review

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

卷:94

Review

Rodrigues, Eugenio¹  Gomes, Alvaro²  Gaspar, Adelio Rodrigues¹  Antunes, Carlos Henggeler² 

[1] Univ Coimbra, Dept Mech Engn, LAETA, ADAI, Rua Luis Reis Santos,Polo 2, P-3030788 Coimbra, Portugal

[2] Univ Coimbra, Dept Elect & Comp Engn, INESC Coimbra, Rua Silvio Lima,Polo 2, P-3030290 Coimbra, Portugal

关键词: Neural network;    Solar variables;    Hydrologic variables;    Atmospheric variables;    Geologic variables;    Climate change;   

DOI  :  10.1016/j.rser.2018.05.060

来源: Elsevier

【 摘 要 】

This paper presents a review on the application of neural networks for the estimation, forecasting, monitoring, and classification of exogenous environmental variables that affect the performance, salubrity, and security of cities, buildings, and infrastructures. The forecast of these variables allows to explore renewable energy and water resources, to prevent potentially hazardous construction locations, and to find the healthiest places, thus promoting a more sustainable future. Five research themes are covered-solar, atmospheric, hydrologic, geologic, and climate change. The solar section comprises solar radiation, direct and diffuse radiation, infrared and ultraviolet radiation, clearness index, and sky luminance and luminous efficacy. The atmospheric section reviews wind, temperature, humidity, cloud classification, and storm prediction. The hydrologic section focuses on precipitation, rainfall-runoff, hail, snow, drought, flood, tides, water levels, and other variables. The geologic section covers works on landslides, earthquakes, liquefaction, erosion, soil classification, soil mechanics, and other properties. Finally, climate change forecasting and downscaling of climate models are reviewed. This work demonstrates the wide range of applications of these methods in different research fields. Some research gaps and interdisciplinary research opportunities are identified for future development of comprehensive forecast and evaluation approaches regarding the estimation of renewable energy and built environment-related variables.

【 授权许可】

Free