Advances on Computational Intelligence in Energy

Dr. Tutut Herawan received a B.Ed degree (2002) and M.Sc degree (2006) in Mathematics from the Universitas Ahmad Dahlan and Universitas Gadjah Mada Yogyakarta, Indonesia, respectively. In 2010, he obtained a PhD in Computer Science from the Universiti Tun Hussein Onn Malaysia. During 2010-2012, he worked as a senior lecturer at the Department of Computer Science and as a co-ordinator of the Data Mining and Knowledge Management research group in the Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang (UMP). Currently a senior lecturer at the Department of Information System, University of Malaya, Herawan has successfully co-supervised 3 PhDs and currently supervises 8 PhDs. Herawan has published papers in various international journals, conference proceedings and book chapters, and has been appointed to the editorial board for MJCS (SCIE), IJDTA, TELKOMNIKA (scopus), IJNCAA, IJDCA and IJDIWC. He has also been appointed as a reviewer of several international journals and as a guest editor for several special issues, such as TELKOMNIKA journal, Applied Soft Computing Elsevier, and IJMISSP Inderscience. Herawan has served as a Chair, PC chair, program committee member and co-organizer for numerous international conferences/workshops. His research area includes data mining and knowledge discovery, decision support in information system, and rough and soft set theory.

Addressing the applications of computational intelligence algorithms in energy, this book presents a systematic procedure that illustrates the practical steps required for applying bio-inspired, meta-heuristic algorithms in energy, such as the prediction of oil consumption and other energy products. 

Contributions include research findings, projects, surveying work and industrial experiences that describe significant advances in the applications of computational intelligence algorithms in energy. For easy understanding, the text provides practical simulation results, convergence and learning curves as well as illustrations and tables.  

Providing a valuable resource for undergraduate and postgraduate students alike, it is also intended for researchers in the fields of computational intelligence and energy.

Basic descriptions of computational intelligence algorithms (single, hybrid, ensemble, integrated and etc.- Credible sources of energy datasets.- Applications of computational algorithms in energy.- Practical application of cuckoo search and neural network in the prediction of OECD oil consumption.- Hybrid of Fuzzy systems and particle swarm optimization in the forecasting gas flaring from oil consumption.- Forecasting of OECD gas flaring using Elman neural network and cuckoo search algorithm.- Artificial bee colony and neural network for the forecasting of Malaysia renewable energy.- Soft computing methods in the modelling of OECD carbon dioxide emission from petroleum consumption.- Modelling energy crises based on Soft computing.- The forecasting of WTI and Dubai crude oil prices benchmarks based on soft computing.- A new approach for the forecasting of IAEA energy.- Modelling of gasoline prices using fuzzy multi-criteria decision making.- Soft computing for the prediction ofAustralia petroleum consumption based on OECD countries.- Future research problems in the area of computational intelligence algorithms in energy.