Forecasting for Ultra-Short-Term Electric Power Load Based on Integrated Artificial Neural Networks

Energy efficiency and renewable energy are the two main research topics for sustainable energy. In the past ten years, countries around the world have invested a lot of manpower into new energy research. However, in addition to new energy development, energy efficiency technologies need to be emphasized to promote production efficiency and reduce environmental pollution. In order to improve power production efficiency, an integrated solution regarding the issue of electric power load forecasting was proposed in this study. The solution proposed was to, in combination with persistence and search algorithms, establish a new integrated ultra-short-term electric power load forecasting method based on the adaptive-network-based fuzzy inference system (ANFIS) and back-propagation neural network (BPN), which can be applied in forecasting electric power load in Taiwan. The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying ANFIS, BPN and persistence. The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and finally the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting. In this paper, the optimal electric power load value was forecasted based on the weighted value obtained therefrom. It was proven through experimental results that the solution proposed in this paper can be used to accurately forecast electric power load, with a minimal error.

[1]  Jianhua Zhang,et al.  An Accurate Very Short-Term Electric Load Forecasting Model with Binary Genetic Algorithm Based Feature Selection for Microgrid Applications , 2018, Electric Power Components and Systems.

[2]  Yuanying Qiu,et al.  Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model , 2018 .

[4]  Jaime Lloret,et al.  A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings , 2014, IEEE Communications Surveys & Tutorials.

[5]  Wayan Suparta,et al.  A comparison of ANFIS and MLP models for the prediction of precipitable water vapor , 2013, 2013 IEEE International Conference on Space Science and Communication (IconSpace).

[6]  Yuan-Kang Wu,et al.  A literature review of wind forecasting technology in the world , 2007, 2007 IEEE Lausanne Power Tech.

[7]  Tao Li,et al.  Review of Evaluation Criteria and Main Methods of Wind Power Forecasting , 2011 .

[8]  Yacine Rezgui,et al.  Electrical load forecasting models: A critical systematic review , 2017 .

[9]  Jian Ma,et al.  A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network , 2018, Energies.

[10]  Angelos K. Marnerides,et al.  Short term power load forecasting using Deep Neural Networks , 2017, 2017 International Conference on Computing, Networking and Communications (ICNC).

[11]  Elsayed E. Hemayed,et al.  Feature selection and optimization of artificial neural network for short term load forecasting , 2016, 2016 Eighteenth International Middle East Power Systems Conference (MEPCON).

[12]  Georg Frey,et al.  Short term load forecasting using hybrid adaptive fuzzy neural system: The performance evaluation , 2017, 2017 IEEE PES PowerAfrica.

[13]  Hsiao-Dong Chiang,et al.  Group-based chaos genetic algorithm and non-linear ensemble of neural networks for short-term load forecasting , 2016 .

[14]  Interactions of Bargaining Power and Introduction of Online Channel in Two Competing Supply Chains , 2018 .