Improved Extreme Learning Machine Power Load Forecasting Based On Firefly Optimization

Improved Extreme Learning Machine Power Load Forecasting Based On Firefly Optimization ...

Improved Extreme Learning Machine Power Load Forecasting Based On Firefly Optimization ... The real value of the power load data will affect the learning accuracy of the extreme learning machine model, increase the learning time, and affect the learning efficiency of the model. Abstract power load forecasting is a crucial safeguard for the reliable, efficient, and safe operation of the power system, which is connected to the smooth operation of society at all levels.

(PDF) Extreme Learning Machine Based On Firefly Adaptive Flower Pollination Algorithm Optimization

(PDF) Extreme Learning Machine Based On Firefly Adaptive Flower Pollination Algorithm Optimization This paper proposes a short term electric load forecasting model with improved extreme learning machine. this model uses eemd to decompose the load sequence into several regular modal components to reduce the error caused by the randomness of the sequence in the load forecasting. The initial weights and thresholds of the extreme learning machine (elm) optimized by the chaotic sparrow search algorithm (cssa) and improved by the firefly algorithm (fa) are used to improve the forecasting performance and achieve accurate forecasting. Short term load forecasting is the basis of power system regulation, and it affects many decisions of power system. in order to deal with the challenge of decli. In this paper, extreme learning machine (elm) model is used as the power load forecasting model. compared with classical forecasting methods, elm model has faster computing speed and stronger generalization ability.

(PDF) Electrical Load Forecasting Using Machine Learning

(PDF) Electrical Load Forecasting Using Machine Learning Short term load forecasting is the basis of power system regulation, and it affects many decisions of power system. in order to deal with the challenge of decli. In this paper, extreme learning machine (elm) model is used as the power load forecasting model. compared with classical forecasting methods, elm model has faster computing speed and stronger generalization ability. Electric load forecasting’s accuracy and reliability are pivotal for enhancing the dispatch efficiency of power systems and the integration of renewable energy into the grid. in response to. This study proposes an enhanced elm model based on artificial intelligence algorithms, integrating the artificial firefly algorithm and artificial fish swarm algorithm. In this study, the ldmoa algorithm optimizes the weights and thresholds of the elm to establish the ldmoa elm prediction model. Finally, the ncso optimization extreme learning machine (ncsoelm) model is used to predict the power load, and compared with back propagation (bp), support vector machine (svm) and cso.

ECBS 1 Feature Selection by Firefly Algorithm with Improved Initialization Strategy