A Forecasting Method of Short-Term Electric Power Load Based on BP Neural Network

Described the meaning of the Short-Term Power forecasting firstly, then gives summary of the basic principles and steps of the power load forecasting, analyses the disadvantages of traditional forecasting methods, and proposing the load analysis plan base on BP neural network theory. Taking full account of the relationship between the daily load and weather factors, establishes a short-term load forecasting model. Results of the prediction are verified highly precise and stable, which makes it suitable for different forecasting conditions.

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A Short-Term Power Load Forecasting Method Based on BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.494-495.1647 ◽

2014 ◽

Vol 494-495 ◽

pp. 1647-1650 ◽

Cited By ~ 4

Author(s):

Ling Juan Li ◽

Wen Huang

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

High Speed ◽

Load Forecasting ◽

High Accuracy ◽

Back Propagation Algorithm ◽

Short Term ◽

Power Load ◽

Forecasting Method ◽

Power Load Forecasting

Short-term power load forecasting is very important for the electric power market, and the forecasting method should have high accuracy and high speed. A three-layer BP neural network has the ability to approximate any N-dimensional continuous function with arbitrary precision. In this paper, a short-term power load forecasting method based on BP neural network is proposed. This method uses the three-layer neural network with single hidden layer as forecast model. In order to improve the training speed of BP neural network and the forecasting efficiency, this method firstly reduces the factors which affect load forecasting by using rough set theory, then takes the reduced data as input variables of the BP neural network model, and gets the forecast value by using back-propagation algorithm. The forecasting results with real data show that the proposed method has high accuracy and low complexity in short-term power load forecasting.

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An evolutionary fuzzy neural network approach for short-term electric power load forecasting

2006 IEEE Power Engineering Society General Meeting ◽

10.1109/pes.2006.1709220 ◽

2006 ◽

Cited By ~ 3

Author(s):

Gwo-Ching Liao

Keyword(s):

Neural Network ◽

Fuzzy Neural Network ◽

Load Forecasting ◽

Network Approach ◽

Short Term ◽

Neural Network Approach ◽

Power Load ◽

Power Load Forecasting ◽

Fuzzy Neural ◽

Electric Power Load

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Short-Term Power Load Forecasting Based on Self-Adapting PSO-BP Neural Network Model

2012 Fourth International Conference on Computational and Information Sciences ◽

10.1109/iccis.2012.279 ◽

2012 ◽

Cited By ~ 6

Author(s):

Yaoyao He ◽

Qifa Xu

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Load Forecasting ◽

Short Term ◽

Power Load ◽

Power Load Forecasting ◽

Bp Neural Network Model

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Short-term power load forecasting based on IVL-BP neural network technology

Systems Engineering Procedia ◽

10.1016/j.sepro.2011.11.062 ◽

2012 ◽

Vol 4 ◽

pp. 168-174 ◽

Cited By ~ 24

Author(s):

Yongli Wang ◽

Dongxiao Niu ◽

Li Ji

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Load Forecasting ◽

Network Technology ◽

Short Term ◽

Power Load ◽

Power Load Forecasting ◽

Neural Network Technology

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Load Prediction of RBF Neural Network Considering Weather Factors

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.397-400.1103 ◽

2013 ◽

Vol 397-400 ◽

pp. 1103-1106

Author(s):

Ren Ran Wei ◽

Zhen Zhu Wei ◽

Hai Bo Yang ◽

Jian Dong Jiang

Keyword(s):

Neural Network ◽

Neural Network Model ◽

Prediction Accuracy ◽

Rbf Neural Network ◽

Load Forecasting ◽

Weather Data ◽

Load Prediction ◽

Short Term ◽

Weather Factors ◽

Power Load

In order to improve the precision of the short-term load prediction, a new method based on radial basis function (RBF) neural network is proposed. The weather data of samples includes the temperature, humidity, date, type, etc., and is quantified according the relevance to load, and then forecasting the power load using RBF neural network model in a region, Actual example shows that this method improves the convergence speed and prediction accuracy of load forecasting.

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Short-term power load forecasting based on BAT-BP neural network model

Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering ◽

10.2991/mmme-16.2016.38 ◽

2016 ◽

Author(s):

Jian Di ◽

Tao Yao

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Load Forecasting ◽

Short Term ◽

Power Load ◽

Power Load Forecasting ◽

Bp Neural Network Model

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

Symmetry ◽

10.3390/sym11081063 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1063 ◽

Cited By ~ 2

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.

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