scholarly journals Medium-term electric power demand forecasting based on economic-electricity transmission model

2018 ◽  
Author(s):  
Wenfeng Li ◽  
Fangmin Bao ◽  
Hongkun Bai ◽  
Wei Liu ◽  
Yongmin Liu ◽  
...  
Keyword(s):  
Electric Power ◽  
Demand Forecasting ◽  
Transmission Model ◽  
Power Demand ◽  
Medium Term ◽  
Electricity Transmission

scholarly journals Short-term electric power demand forecasting based on economic-electricity transmission model

2018 ◽  
Author(s):  
Wenfeng Li ◽  
Hongkun Bai ◽  
Wei Liu ◽  
Yongmin Liu ◽  
Yubin Mao Wang ◽  
...  
Keyword(s):  
Electric Power ◽  
Demand Forecasting ◽  
Transmission Model ◽  
Power Demand ◽  
Short Term ◽  
Electricity Transmission

A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon

2017 ◽  
Vol 142 ◽  
pp. 58-73 ◽  
Author(s):  
Kianoosh G. Boroojeni ◽  
M. Hadi Amini ◽  
Shahab Bahrami ◽  
S.S. Iyengar ◽  
Arif I. Sarwat ◽  
...  
Keyword(s):  
Electric Power ◽  
Time Scale ◽  
Demand Forecasting ◽  
Power Demand ◽  
Medium Term ◽  
Short Term ◽  
Scale Modeling

scholarly journals Electric Power Demand Forecasting of KAVAL Cities

2010 ◽  
Vol 4 (2) ◽  
pp. 85-89 ◽  
Author(s):  
A.K. Bhardwaj ◽  
R.C. Bansal ◽  
R.K. Saket ◽  
A.K. Srivastava
Keyword(s):  
Electric Power ◽  
Demand Forecasting ◽  
Power Demand

The Research on Index System of Economic Society Development Based on the Medium and Long-Term Power Demand Forecasting

2014 ◽  
Vol 672-674 ◽  
pp. 2146-2152
Author(s):  
Shan Shan Wu ◽  
Xin Yang Han ◽  
Wan Lei Xue
Keyword(s):  
Economic Development ◽  
Electric Power ◽  
Index System ◽  
Demand Forecasting ◽  
Power Demand ◽  
The Index System ◽  
Relationship Of ◽  
Cointegration Relationship

In this paper, we firstly review the paper related to index system of economic society development, and divide and analyze the stage of Chinese economic development by using the theory of Chenery, Hoffman and Lewis. Then we selected indicators by using GRA and FA, and also consider the cointegration relationship of these indicators with power. Finally, we propose the index system of economic society development based on the medium and long-term electric power demand forecasting, which included 3 first-level indexes, 7 second-level indicators.

scholarly journals Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasting at PT. PLN Gresik Indonesia

2018 ◽  
Vol 8 (6) ◽  
pp. 4892
Author(s):  
Ismit Mado ◽  
Adi Soeprijanto ◽  
Suhartono Suhartono
Keyword(s):  
Power Generation ◽  
Electric Power ◽  
Arima Model ◽  
Electrical Power ◽  
Demand Forecasting ◽  
Future Research ◽  
Power Demand ◽  
Generation System ◽  
Electricity Load ◽  
Power Generation System

The prediction of the use of electric power is very important to maintain a balance between the supply and demand of electric power in the power generation system. Due to a fluctuating of electrical power demand in the electricity load center, an accurate forecasting method is required to maintain the efficiency and reliability of power generation system continuously. Such conditions greatly affect the dynamic stability of power generation systems. The objective of this research is to propose Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) to predict electricity load. Half hourly load data for of three years period at PT. PLN Gresik Indonesia power plant unit are used as case study. The parameters of DSARIMA model are estimated by using least squares method. The result shows that the best model to predict these data is subset DSARIMA with order ([1,2,7,16,18,35,46],1,[1,3,13,21,27,46])(1,1,1)48(0,0,1)336 with MAPE about 2.06%. Thus, future research could be done by using these predictive results as models of optimal control parameters on the power system side.

scholarly journals Accurate Demand Forecasting: A Flexible and Balanced Electric Power Production Big Data Virtualization Based on Photovoltaic Power Plant

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6915
Author(s):  
Seung-Mo Je ◽  
Hyeyoung Ko ◽  
Jun-Ho Huh
Keyword(s):  
Big Data ◽  
Renewable Energy ◽  
Power Plant ◽  
Electric Power ◽  
Demand Forecasting ◽  
Power Production ◽  
Supply System ◽  
Power Demand ◽  
Photovoltaic Power ◽  
Electric Power Production

This paper has tried to execute accurate demand forecasting by utilizing big data visualization and proposes a flexible and balanced electric power production big data virtualization based on a photovoltaic power plant. First of all, this paper has tried to align electricity demand and supply as much as possible using big data. Second, by using big data to predict the supply of new renewable energy, an attempt was made to incorporate new and renewable energy into the current power supply system and to recommend an efficient energy distribution method. The first presented problem that had to be solved was the improvement in the accuracy of the existing electricity demand for forecasting models. This was explained through the relationship between the power demand and the number of specific words in the paper that use crawling by utilizing big data. The next problem arose because the current electricity production and supply system stores the amount of new renewable energy by changing the form of energy that is produced through ESS or that is pumped through water power generation without taking the amount of new renewable energy that is generated from sources such as thermal power, nuclear power, and hydropower into consideration. This occurs due to the difficulty of predicting power production using new renewable energy and the absence of a prediction system, which is a problem due to the inefficiency of changing energy types. Therefore, using game theory, the theoretical foundation of a power demand forecasting model based on big data-based renewable energy production forecasting was prepared.

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