FUKUYAMA Yoshikazu    Department   Undergraduate School  , School of Interdisciplinary Mathematical Sciences    Position   Professor
Date	2018/09/27
Presentation Theme	Daily Peak Load Forecasting by Artificial Neural Network using Correntropy based Differential Evolutionary Particle Swarm Optimization for Reduction of Engineering on Data Including Untypical Values
Conference	Workshop on Electric Power Technology and Power System Technology of IEE of Japan
Promoters	Technical Committees of Electric Power Technology and Power System of Power & Energy Society of IEE of Japan
Conference Type	Workshop/Symposium
Presentation Type	Speech (General)
Contribution Type	Collaborative
Venue	Nagoya Institute of Technology
Publisher and common publisher	Daiji Sakurai, Yoshikazu Fukuyama, Tatsuya Iizaka, Tetsuro Matsui,
Details	This paper proposes daily peak load forecasting by Artificial Neural Network (ANN) using correntropy based differential evolutionary particle swarm optimization (DEEPSO) for reduction of engineering on data including untypical values. When untypical data exist in the training data, estimation accuracy of daily peak load forecasting can be affected by the untypical data. Therefore, engineers have to remove the untypical values in order improve estimation accuracy. It is a heavy burden to engineers. Correntropy has a possibility to solve this problem. The effectiveness of proposed method is verified by comparison with conventional least mean squre (LMS) based ANNs trained using back-propagation (BP), particle swarm optimization (PSO), and DEEPSO.