Prediction of Photovoltaic Panels Output Performance Using Artificial Neural Network

被引:0
|
作者
Ardebili, Anis Asadi [1 ]
Saez, Paola Villoria [2 ]
Cortina, Mariano Gonzalez [1 ]
Cruz, Dany Marcelo Tasan [1 ]
Saiz, Angel Rodriguez [3 ]
Atanes-Sanchez, Evangelina [4 ]
机构
[1] Univ Politecn Madrid, Escuela Tecn Super Edificac, Madrid, Spain
[2] Univ Politecn Madrid, Escuela Tecn Super Edificac, Grp Invest Tecnol Edificatoria & Medio Ambiente T, Madrid, Spain
[3] Univ Burgos, Escuela Politecn Super, Grp Invest Ingn Edificac GIIE, Burgos, Spain
[4] Univ Politecn Madrid, Escuela Tecn Super Ingn & Diseno Ind, Grp Invest Caracterizac Opt Mat ACOM, Madrid, Spain
关键词
Plaster; Recycling; Building; Circular economy; Polyurethane; Fiberglass; Cardboard; DEMOLITION WASTE; GYPSUM COMPOSITES; CONSTRUCTION; RUBBER; PLASTERBOARD; CHALLENGES; MANAGEMENT; MORTARS; IMPACT; CDW;
D O I
10.1016/j.conbuildmat.2023.130675
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The construction sector is one of the main industries generating greater environmental impacts. In this sense, the European Commission is forcing the sector to implement alternative measurement and strategies to tackle this situation and bring the sector to a circular economy. One of the adopted measures is the use of recycled materials to produce construction materials and products. In this sense, many scientific works have been conducted analyzing the incorporation of different waste categories in gypsum products. In this sense, the main objective of this research is to characterize new gypsum-based materials that incorporate waste from the automotive sector. For this, mixed waste (containing polyurethane, cardboard and fiberglass) obtained during the production of automobiles' backboards was used. A total of 171 specimens were produced incorporating different percentage and size of mixed waste. These specimens were tested according to the bulk density, superficial hardness, and flexural, compressive and bonding strengths. Results show that it is possible to incorporate up to 11% of mixed waste overpassing the minimum strength values established by the regulations. In addition, the lightness of the material and its compression and flexion behavior improved considerably compared to the reference specimens without any waste addition.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] Terrorism prediction using artificial neural network
    Soliman G.M.A.
    Abou-El-Enien T.H.M.
    Revue d'Intelligence Artificielle, 2019, 33 (02) : 81 - 87
  • [42] Prediction of Diabetes by using Artificial Neural Network
    Sapon, Muhammad Akmal
    Ismail, Khadijah
    Zainudin, Suehazlyn
    CIRCUITS, SYSTEM AND SIMULATION, 2011, 7 : 299 - 303
  • [43] Generation of Photovoltaic Output Power Forecast Using Artificial Neural Networks
    Elamim, A.
    Hartiti, B.
    Haibaoui, A.
    Lfakir, A.
    Thevenin, P.
    ADVANCED INTELLIGENT SYSTEMS FOR SUSTAINABLE DEVELOPMENT (AI2SD'2019): VOL 7 - ADVANCED INTELLIGENT SYSTEMS FOR SUSTAINABLE DEVELOPMENT APPLIED IN ENERGY AND ELECTRICAL ENGINEERING, 2020, 624 : 127 - 134
  • [44] Sensor network proposal based on IoT for a prediction system of the power output from photovoltaic panels
    Vestenicky, Martin
    Matuska, Slavomir
    Hudec, Robert
    Kamencay, Patrik
    2018 28TH INTERNATIONAL CONFERENCE RADIOELEKTRONIKA (RADIOELEKTRONIKA), 2018,
  • [45] Prediction of performance and smoke emission using artificial neural network in a diesel engine
    Sekmen, Yakup
    Gölcü, Mustafa
    Erduranli, Perihan
    Pancar, Yaşar
    Mathematical and Computational Applications, 2006, 11 (03) : 205 - 214
  • [46] Performance Prediction of Diamond Sawblades Using Artificial Neural Network and Regression Analysis
    Aydin, Gokhan
    Karakurt, Izzet
    Hamzacebi, Coskun
    ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2015, 40 (07) : 2003 - 2012
  • [47] Performance Prediction of Diamond Sawblades Using Artificial Neural Network and Regression Analysis
    Gokhan Aydin
    Izzet Karakurt
    Coskun Hamzacebi
    Arabian Journal for Science and Engineering, 2015, 40 : 2003 - 2012
  • [48] Prediction of Performance Parameters of Stratified TES Tank Using Artificial Neural Network
    Soomro, Afzal Ahmed
    Mokhtar, Ainul Akmar
    6TH INTERNATIONAL CONFERENCE ON PRODUCTION, ENERGY AND RELIABILITY 2018: WORLD ENGINEERING SCIENCE & TECHNOLOGY CONGRESS (ESTCON), 2018, 2035
  • [49] Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM)
    Arash Ebrahimabadi
    Mohammad Azimipour
    Ali Bahreini
    Journal of Rock Mechanics and Geotechnical Engineering, 2015, 7 (05) : 573 - 583
  • [50] Prediction of schedule performance of Indian construction projects using an artificial neural network
    Jha, Kumar Neeraj
    Chockalingam, Ct
    CONSTRUCTION MANAGEMENT AND ECONOMICS, 2011, 29 (09) : 901 - 911