An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

Abstract

The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids and energy management on the load side. Microgrid can effectively solve this problem by using its regulation and flexibility, and is considered to be an ideal platform. The traditional method of computing total transfer capability is difficult due to the central integration of wind farms. As a result, the differential evolution extreme learning machine is offered as a data mining approach for extracting operating rules for the total transfer capability of tie-lines in wind-integrated power systems. K-medoids clustering under the two-dimensional "wind powerload consumption" feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, a knowledge base for total transfer capability operating rule mining is created. Then, a novel method is used to filter redundant characteristics and find features that are closely associated to the total transfer capability in order to decrease the ultra-high dimensionality of operational features. Finally, by feeding the training data into the proposed algorithm, the total transfer capability operation rules are derived from the knowledge base. It can be seen that, the proposed algorithm can optimize the system performance with good accuracy and generality, according to numerical data.

SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

“…Machine learning (ML) [8][9][10], particularly convolutional neural networks (CNNs) [11][12][13], has shown encouraging results in medical image analysis tasks. However, the high number of parameters in most used models may hinder their widespread use in clinical settings.…”

Partición de una Red Eléctrica de Distribución Aplicando Algoritmos de Agrupamiento K-means y DBSCAN

“…La potencia de transmisión límite o la capacidad de transmisión total es uno de los indicadores de operación clave o más importantes dentro de una red eléctrica interconectada. En los últimos años, el desarrollo y la amplia aplicación de tecnologías de big data e inteligencia artificial han proporcionado nuevos medios técnicos para el modelado, operación y distribución de energía en redes eléctricas [11].…”

On the Modeling of Energy-Multisource Networks by the Thermostatted Kinetic Theory Approach: A Review with Research Perspectives

“…The stochastic role of the networks has also been taken into account by employing methods based on probability; see [22][23][24][25]. Approaches based on the artificial intelligence have also been proposed; see papers [26][27][28][29] and the reference cited therein. Differential equations-based models have also been derived [30][31][32][33], along with Petri Net frameworks [34] and topological structures [35,36].…”