A Hybrid Double Forecasting System of Short Term Power Load Based on Swarm Intelligence and Nonlinear Integration Mechanism

2020 ◽  
Vol 10 (4) ◽  
pp. 1550 ◽  
Author(s):  
Ping Jiang ◽  
Ying Nie
Keyword(s):  
Load Forecasting ◽  
Environmental Benefits ◽  
Good Effect ◽  
Bp Network ◽  
Important Place ◽  
Interval Prediction ◽  
Power Load ◽  
Integration Mechanism ◽  
Forecasting System ◽  
Nonlinear Integration

Accurate and reliable power load forecasting not only takes an important place in management and steady running of smart grid, but also has environmental benefits and economic dividends. Accurate load point forecasting can provide a guarantee for the daily operation of the power grid, and effective interval forecasting can further quantify the uncertainty of power load on this basis to provide dependable and precise load information. However, most of the previous work focuses on the deterministic point prediction of power load and rarely considers the interval prediction of power load, which makes the prediction of power load not comprehensive. In this study, a new double hybrid load forecasting system including point forecasting module and interval forecasting module is developed, which can make up for the shortcomings of incomplete analysis for the existing research. The point forecasting module adopts a nonlinear integration mechanism based on Back Propagation (BP) network optimized by Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) to improve the accuracy of point prediction. A fuzzy clustering interval prediction method based on different data feature classification is successfully proposed which provides an effective tool for load uncertainty analysis. The experiment results show that the system not only has a good effect in accurately predicting power load, but also can analyze the uncertainty of the power load, which can be used as an effective technology of power system planning.

An Energy-Image Based Multi-Unit Power Load Forecasting System

2018 ◽  
Author(s):  
Chengpei Tang ◽  
Shanqing Wang ◽  
Chancheng Zhou ◽  
Xiaolong Zheng ◽  
Hua Li ◽  
...  
Keyword(s):  
Load Forecasting ◽  
Unit Power ◽  
Power Load ◽  
Power Load Forecasting ◽  
Forecasting System

Implementation of Power Load Forecasting Based on the Improved Neural Network Model

2015 ◽  
Vol 734 ◽  
pp. 468-471 ◽  
Author(s):  
Yang Xu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Elman Network ◽  
Bp Network ◽  
Power Load ◽  
Power Load Forecasting

This paper introduces the importance of power load forecasting, and makes forecasting based on the power load values collected in Botou City within a week. The conclusion shows that the accuracy of PSO - ELMAN network forecasting results is much higher than that of PSO - BP network forecasting results.

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 723-728 ◽  
Author(s):  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Yan Lu ◽  
Mian Xing
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Load Forecasting ◽  
Optimal Solution ◽  
Electric Power System ◽  
Local Optimum ◽  
Good Prediction ◽  
Ant Colony Optimization Algorithm ◽  
The Neural Network ◽  
Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

scholarly journals Forecasting for Ultra-Short-Term Electric Power Load Based on Integrated Artificial Neural Networks

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1063 ◽  
Author(s):  
Horng-Lin Shieh ◽  
Fu-Hsien Chen
Keyword(s):  
Energy Efficiency ◽  
Electric Power ◽  
Production Efficiency ◽  
Load Forecasting ◽  
Short Term ◽  
Inference System ◽  
Power Load ◽  
Power Load Forecasting ◽  
New Energy ◽  
Electric Power Load

Energy efficiency and renewable energy are the two main research topics for sustainable energy. In the past ten years, countries around the world have invested a lot of manpower into new energy research. However, in addition to new energy development, energy efficiency technologies need to be emphasized to promote production efficiency and reduce environmental pollution. In order to improve power production efficiency, an integrated solution regarding the issue of electric power load forecasting was proposed in this study. The solution proposed was to, in combination with persistence and search algorithms, establish a new integrated ultra-short-term electric power load forecasting method based on the adaptive-network-based fuzzy inference system (ANFIS) and back-propagation neural network (BPN), which can be applied in forecasting electric power load in Taiwan. The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying ANFIS, BPN and persistence. The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and finally the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting. In this paper, the optimal electric power load value was forecasted based on the weighted value obtained therefrom. It was proven through experimental results that the solution proposed in this paper can be used to accurately forecast electric power load, with a minimal error.

scholarly journals Power load forecasting research based on neural network and Holt-winters method

2021 ◽  
Vol 692 (2) ◽  
pp. 022120
Author(s):  
Jianjun Fan ◽  
Xinzhong Liu ◽  
Zhimin Li ◽  
Xinku Wang ◽  
Shengnan Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Power Load ◽  
Power Load Forecasting
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