Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Forecasting exchange rates using general regression neural networks
Computers and Operations Research - Neural networks in business
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Methodology for long-term prediction of time series
Neurocomputing
Genetic optimization of GRNN for pattern recognition without feature extraction
Expert Systems with Applications: An International Journal
Motion control with deadzone estimation and compensation using GRNN for TWUSM drive system
Expert Systems with Applications: An International Journal
Power load forecasting using support vector machine and ant colony optimization
Expert Systems with Applications: An International Journal
A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example
Knowledge-Based Systems
A general regression neural network
IEEE Transactions on Neural Networks
A collaborative fuzzy-neural approach for long-term load forecasting in Taiwan
Computers and Industrial Engineering
Fuzzy artificial neural network p, d, q model for incomplete financial time series forecasting
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Accurate annual power load forecasting can provide reliable guidance for power grid operation and power construction planning, which is also important for the sustainable development of electric power industry. The annual power load forecasting is a non-linear problem because the load curve shows a non-linear characteristic. Generalized regression neural network (GRNN) has been proven to be effective in dealing with the non-linear problems, but it is very regretfully finds that the GRNN have rarely been applied to the annual power load forecasting. Therefore, the GRNN was used for annual power load forecasting in this paper. However, how to determine the appropriate spread parameter in using the GRNN for power load forecasting is a key point. In this paper, a hybrid annual power load forecasting model combining fruit fly optimization algorithm (FOA) and generalized regression neural network was proposed to solve this problem, where the FOA was used to automatically select the appropriate spread parameter value for the GRNN power load forecasting model. The effectiveness of this proposed hybrid model was proved by two experiment simulations, which both show that the proposed hybrid model outperforms the GRNN model with default parameter, GRNN model with particle swarm optimization (PSOGRNN), least squares support vector machine with simulated annealing algorithm (SALSSVM), and the ordinary least squares linear regression (OLS_LR) forecasting models in the annual power load forecasting.