TY - BOOK ID - 8284667 TI - Feasibility Model of Solar Energy Plants by ANN and MCDM Techniques AU - Majumder, Mrinmoy. AU - Saha, Apu K. PY - 2016 SN - 9812873074 9812873082 PB - Singapore : Springer Singapore : Imprint: Springer, DB - UniCat KW - Mechanical Engineering - General KW - Mechanical Engineering KW - Engineering & Applied Sciences KW - Solar energy KW - Computer simulation. KW - Decision making. KW - Solar power KW - Energy. KW - Renewable energy resources. KW - Climate change. KW - Electric power production. KW - Computational intelligence. KW - Renewable energy sources. KW - Alternate energy sources. KW - Green energy industries. KW - Environmental economics. KW - Renewable and Green Energy. KW - Computational Intelligence. KW - Energy Technology. KW - Environmental Economics. KW - Climate Change/Climate Change Impacts. KW - Economics KW - Environmental quality KW - Green energy industries KW - Energy industries KW - Alternate energy sources KW - Alternative energy sources KW - Energy sources, Renewable KW - Sustainable energy sources KW - Power resources KW - Renewable natural resources KW - Agriculture and energy KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing KW - Electric power generation KW - Electricity generation KW - Power production, Electric KW - Electric power systems KW - Electrification KW - Changes, Climatic KW - Climate change KW - Climate changes KW - Climate variations KW - Climatic change KW - Climatic changes KW - Climatic fluctuations KW - Climatic variations KW - Global climate changes KW - Global climatic changes KW - Climatology KW - Climate change mitigation KW - Teleconnections (Climatology) KW - Environmental aspects KW - Economic aspects KW - Force and energy KW - Renewable energy sources KW - Solar radiation KW - Engineering. KW - Energy Systems. KW - Construction KW - Industrial arts KW - Technology KW - Energy systems. KW - Changes in climate KW - Climate change science KW - Global environmental change UR - https://www.unicat.be/uniCat?func=search&query=sysid:8284667 AB - This Brief highlights a novel model to find out the feasibility of any location to produce solar energy. The model utilizes the latest multi-criteria decision making techniques and artificial neural networks to predict the suitability of a location to maximize allocation of available energy for producing optimal amount of electricity which will satisfy the demand from the market. According to the results of the case studies further applications are encouraged. ER -