Power Load Forecasting Using Improved Grey-Markov Method

2014 ◽  
Vol 1006-1007 ◽  
pp. 976-981
Author(s):  
Jie Xu ◽  
Yuan Sheng Huang
Keyword(s):  
Load Forecasting ◽  
Original Data ◽  
Small Sample ◽  
Gray System Theory ◽  
Gray System ◽  
Power Construction ◽  
Power Load ◽  
Power Load Forecasting ◽  
Grid Operation ◽  
Markov Method

Power load forecasting is an important part of management modernization of power system. Accurate load forecasting can provide reliable guidance for grid operation and power construction planning. For load forecasting "small sample", "poor information", "uncertain", "non-linear" and other features, In this paper, GM (1.1) model was improved in the gray system theory, by constructing the background value sequence to transform the original data, using gray rolling GM (1.1) model and combining Markov prediction model to make power load forecasting. The application results show that this method is accurate and practicable.

scholarly journals A Power Load Forecasting Model Based on FA-CSSA-ELM

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zuoxun Wang ◽  
Xinheng Wang ◽  
Chunrui Ma ◽  
Zengxu Song
Keyword(s):  
Search Algorithm ◽  
Load Forecasting ◽  
Local Optimum ◽  
Chaotic Mapping ◽  
Forecasting Models ◽  
Power Resources ◽  
Power Load ◽  
Power Load Forecasting ◽  
Learning Machine ◽  
Grid Operation

Accurate and stable power load forecasting methods are essential for the rational allocation of power resources and grid operation. Due to the nonlinear nature of power loads, it is difficult for a single forecasting method to complete the forecasting task accurately and quickly. In this study, a new combined model for power loads forecasting is proposed. 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. The early local optimum that exists in the sparrow algorithm is overcome by Tent chaotic mapping. A firefly perturbation strategy is used to improve the global optimization capability of the model. Real values from a power grid in Shandong are used to validate the prediction performance of the proposed FA-CSSA-ELM model. Experiments show that the proposed model produces more accurate forecasting results than other single forecasting models or combined forecasting models.

The Application of Improved GM(1,1) in Power Load Forecasting

2010 ◽  
Vol 108-111 ◽  
pp. 151-155
Author(s):  
Cheng Xiang Fan ◽  
Kai Quan Shi ◽  
Ke Jun Li
Keyword(s):  
Load Forecasting ◽  
Original Data ◽  
Data Sequence ◽  
Proposed Model ◽  
Power Load ◽  
Forecasting Method ◽  
Power Load Forecasting ◽  
Novel Method

The forecasting precision of GM(1,1) is very low, when the data sequence is not smooth. The logarithm smoothing is used for the original data sequence. Considering the low precision caused by overlarge and forecasting gray interval for gray modeling, A novel method is proposed for power load forecasting: weighted forecasting method of gray related degree with revised parameter and logarithm smoothing. The method can make various factors weaken or counteracted and prevent the forecasting data from too fast increasing. The proposed model is demonstrated by a test in a certain area. The result shows that the method is effective both in theory and in practice.

Short-Term Power Load Forecasting Based on EMD and ESN

2013 ◽  
Vol 651 ◽  
pp. 910-916
Author(s):  
Yong Luo ◽  
Xue Jia ◽  
Shu Wei Chen
Keyword(s):  
Prediction Accuracy ◽  
Load Forecasting ◽  
Original Data ◽  
Echo State Network ◽  
Short Term ◽  
Independent Components ◽  
Mode Decomposition ◽  
Power Load ◽  
Power Load Forecasting ◽  
Network Method

With the continuous development of power market, the precision requirement for short-term power load forecasting is constantly being improved. In order to obtain higher prediction accuracy, this paper put forward a method of combining empirical mode decomposition (EMD) with echo state network (ESN) for short-term power load forecasting. First, original data had been decomposed into several independent components, whose features were obvious. A corresponding echo state network was built for each component. Then, each component should be trained and predicted by its corresponding echo state network. The experimental results showed that this method has a better prediction accuracy compared with traditional neural network method.

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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