Short-term photovoltaic power forecasting based on human body amenity and least squares support vector machine with fruit fly optimization algorithm

2017 ◽  
Vol 19 (5-6) ◽  
pp. 375-390
Author(s):  
Huabao CHEN ◽  
Ling CHEN ◽  
Wei HAN
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Human Body ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Short Term ◽  
Fruit Fly Optimization ◽  
Photovoltaic Power

scholarly journals Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2226 ◽  
Author(s):  
Ming-Wei Li ◽  
Jing Geng ◽  
Wei-Chiang Hong ◽  
Yang Zhang
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Strong Nonlinearity ◽  
Electric Load ◽  
Large Power ◽  
Fruit Fly Optimization

Compared with a large power grid, a microgrid electric load (MEL) has the characteristics of strong nonlinearity, multiple factors, and large fluctuation, which lead to it being difficult to receive more accurate forecasting performances. To solve the abovementioned characteristics of a MEL time series, the least squares support vector machine (LS-SVR) hybridizing with meta-heuristic algorithms is applied to simulate the nonlinear system of a MEL time series. As it is known that the fruit fly optimization algorithm (FOA) has several embedded drawbacks that lead to problems, this paper applies a quantum computing mechanism (QCM) to empower each fruit fly to possess quantum behavior during the searching processes, i.e., a QFOA algorithm. Eventually, the cat chaotic mapping function is introduced into the QFOA algorithm, namely CQFOA, to implement the chaotic global perturbation strategy to help fruit flies to escape from the local optima while the population’s diversity is poor. Finally, a new MEL forecasting method, namely the LS-SVR-CQFOA model, is established by hybridizing the LS-SVR model with CQFOA. The experimental results illustrate that, in three datasets, the proposed LS-SVR-CQFOA model is superior to other alternative models, including BPNN (back-propagation neural networks), LS-SVR-CQPSO (LS-SVR with chaotic quantum particle swarm optimization algorithm), LS-SVR-CQTS (LS-SVR with chaotic quantum tabu search algorithm), LS-SVR-CQGA (LS-SVR with chaotic quantum genetic algorithm), LS-SVR-CQBA (LS-SVR with chaotic quantum bat algorithm), LS-SVR-FOA, and LS-SVR-QFOA models, in terms of forecasting accuracy indexes. In addition, it passes the significance test at a 97.5% confidence level.

Sign in / Sign up

Export Citation Format

Share Document