Electrical Load Forecasting Based on Multi-model Combination by Stacking Ensemble Learning Algorithm
- Resource Type
- Conference
- Authors
- Jiang, Jianfeng; Zhu, Wenjun; Zhang, Chong; Xingang, Wang
- Source
- 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Artificial Intelligence and Computer Applications (ICAICA), 2021 IEEE International Conference on. :739-743 Jun, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Machine learning algorithms
Load forecasting
Computational modeling
Stacking
Linear regression
Learning (artificial intelligence)
Predictive models
load forecasting
stacking
ensemble learning
- Language
Load forecasting is helpful to achieve the goals of emission reduction and the balance of power generation and consumption. In this paper, a load forecasting method based on multi-model combination by Stacking ensemble method was proposed. The most appropriate basic models were chosen as the basic learners in order to achieve the optimal performance of Stacking model. The second layer choose the model based on a simple algorithm to prevent over fitting. Some representative load data are selected to verify the feasibility of the algorithm. The results show that the Stacking learning framework improves the overall prediction accuracy by optimizing the output results of multiple models, has a good application effect in power load prediction.