Study of Super Short-Term Bus Load Forecasting Model Based on Similar Ranges

2014 ◽  
Vol 492 ◽  
pp. 482-488
Author(s):  
Dong Xiao Niu ◽  
Yan Chao Chen ◽  
Jiao Fan ◽  
Qing Guo Ma ◽  
Qin Liang Tan
Keyword(s):  
Electric Power ◽  
Emission Reduction ◽  
Load Forecasting ◽  
The Other ◽  
Forecasting Model ◽  
Network Computing ◽  
Short Term ◽  
Increasing Demand ◽  
One Year ◽  
Safety And Stability

With the increasing demand of electricity dispatching and the request of energy conservation and emission reduction, the dispatching plan and operating of electric power has become more and more important. On one hand, electric power companies try to reduce power reserve as much as possible to increase the efficiency; on the other hand, some power reserve is necessary to deal with emergency and to ensure the safety and stability of grid. Therefore, this paper proposes a super short-term bus load forecasting model which is based on similar ranges to track the variation of weather and load. By using the method of fruit flies optimizing grey neural network in the real time, it can reduce the size of the network computing and solve the problem of divergence. Since the system based on the model has operated for one year, it proves that this model can meet the requirement of the precision for the electricity dispatching and adapt to the changes in different regions.

User-Level Ultra-Short-Term Load Forecasting Model Based on Optimal Feature Selection and Bahdanau Attention Mechanism

2021 ◽  
pp. 2150279
Author(s):  
Ziyao Wang ◽  
Huaqiang Li ◽  
Zizhuo Tang ◽  
Yang Liu
Keyword(s):  
Feature Selection ◽  
Power Consumption ◽  
Electric Power ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Optimal Feature

Accurate ultra-short-term load forecasting is of great significance for real-time power generation scheduling and development of power cyber physical systems (Power CPS). However, in order to forecast the future load using the current high-dimensional, diverse and heterogeneous electric power consumption information, new challenges have been raised to the effective feature selection and the accurate load forecasting algorithms. However, very limited existing works consider the feature selection for the electric power consumption information and impacts to the thereafter load forecasting model. In view of this point, features that are critical to the load forecasting are selected using an embedded feature selection algorithm based on LightGBM to form an optimal feature set, with which a sequence to sequence (S2S) and gated recurrent unit (GRU)-based ultra-short-term load forecasting model that incorporates Bahdanau attention (BA) mechanism is presented. The S2S-GRU model is based on an encoding–decoding framework that is compatible to the input and output data series with variable lengths. By introducing the BA mechanism, loss of previous information issue of GRU can be solved. Experimental results show that first the presented feature selection algorithm can help to improve the performance of the load forecasting model. Second, the presented load forecasting model can find a compromise between the forecasting efficiency and accuracy.

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 Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization

2018 ◽  
Vol 8 (6) ◽  
pp. 864 ◽  
Author(s):  
Murat Luy ◽  
Volkan Ates ◽  
Necaattin Barisci ◽  
Huseyin Polat ◽  
Ertugrul Cam
Keyword(s):  
Knowledge Base ◽  
Load Forecasting ◽  
Ant Colony ◽  
Forecasting Model ◽  
Short Term ◽  
Fuzzy Knowledge

scholarly journals Short-term Load Forecasting Model Considering Meteorological Factors

2019 ◽  
Vol 118 ◽  
pp. 02050
Author(s):  
Xi Yunhua ◽  
Zhu Haojun ◽  
Dong Nan
Keyword(s):  
Decision Tree ◽  
Meteorological Factors ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Analysis Method ◽  
Decision Tree Algorithm ◽  
Characteristic Analysis ◽  
Short Term ◽  
Tree Algorithm ◽  
Short Term Load Forecasting

Because of the limitation of basic data and processing methods, the traditional load characteristic analysis method can not achieve user-level refined prediction. This paper builds a user-level short-term load forecasting model based on algorithms such as decision trees and neural networks in big data technology. Firstly, based on the grey relational analysis method, the influence of meteorological factors on load characteristics is quantitatively analyzed. The key factors are selected as input vectors of decision tree algorithm. This paper builds a category label for each daily load curve after clustering the user’s historical load data. The decision tree algorithm is used to establish classification rules and classify the days to be predicted. Finally, Elman neural network is used to predict the short-term load of a user, and the validity of the model is verified.

Day-ahead load forecasting using improved grey Verhulst model

2020 ◽  
Vol 18 (5) ◽  
pp. 1335-1348
Author(s):  
Ariel Mutegi Mbae ◽  
Nnamdi I. Nwulu
Keyword(s):  
Power Systems ◽  
Unit Commitment ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Short Term ◽  
Content Type ◽  
Dispatch System ◽  
System Maintenance ◽  
Electricity Load ◽  
Generator Unit

Purpose In the daily energy dispatch process in a power system, accurate short-term electricity load forecasting is a very important tool used by spot market players. It is a critical requirement for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The purpose of this study is to present an improved grey Verhulst electricity load forecasting model. Design/methodology/approach To test the effectiveness of the proposed model for short-term load forecast, studies made use of Kenya’s load demand data for the period from January 2014 to June 2019. Findings The convectional grey Verhulst forecasting model yielded a mean absolute percentage error of 7.82 per cent, whereas the improved model yielded much better results with an error of 2.96 per cent. Practical implications In the daily energy dispatch process in a power system, accurate short-term load forecasting is a very important tool used by spot market players. It is a critical ingredient for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The fact that the model uses actual Kenya’s utility data confirms its usefulness in the practical world for both economic planning and policy matters. Social implications In terms of generation and transmission investments, proper load forecasting will enable utilities to make economically viable decisions. It forms a critical cog of the strategic plans for power utilities and other market players to avoid a situation of heavy stranded investment that adversely impact the final electricity prices and the other extreme scenario of expensive power shortages. Originality/value This research combined the use of natural logarithm and the exponential weighted moving average to improve the forecast accuracy of the grey Verhulst forecasting model.

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