Electric Power Demand Forecasting of KAVAL Cities

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.

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

10.1063/1.5041148 ◽

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

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An approach of electric power demand forecasting using data-mining method: a case study of application of data-mining technique to improve decision making

International Journal of Management and Decision Making ◽

10.1504/ijmdm.2006.008173 ◽

2006 ◽

Vol 7 (1) ◽

pp. 88 ◽

Cited By ~ 3

Author(s):

Toshio Sugihara

Keyword(s):

Data Mining ◽

Decision Making ◽

Electric Power ◽

Demand Forecasting ◽

Mining Method ◽

Power Demand ◽

Data Mining Technique ◽

Mining Technique ◽

Using Data

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

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i6.pp4892-4901 ◽

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.

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

Energies ◽

10.3390/en14216915 ◽

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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