Open AccessThe Load Forecasting Method Based on Adaptive Neural Network and TLBO Algorithm
Author(s) -
Xiu Yang,
Lei Wang,
Yukun Liu,
Baodong Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1549/5/052113
Subject(s) - artificial neural network , computer science , mathematical optimization , convergence (economics) , population , local optimum , algorithm , artificial intelligence , mathematics , demography , sociology , economics , economic growth
Load forecasting is of great significance for the arrangement and optimization of the distribution network scheduling. This paper proposes a load forecasting method based on an improved TLBO optimized adaptive neural network. Firstly, the ‘teaching’ phase in the basic TLBO algorithm is improved, and the average value of all search individuals is changed while adopting adaptive teaching factors, so that the performance of TLBO in the entire search space can be adaptively improved. Then, the ‘learning’ phase of the TLBO is improved. The Gaussian mutation operator is introduced in the learning phase to maintain the diversity of the population and avoid the TLBO algorithm’s premature convergence and local optimization. Finally, using the improved TLBO algorithm to optimize the adaptive neural network forecast model. In the end, the actual load data of Tianjin Power Grid was used to verify the accuracy of the simulation results.