scholarly journals A Survey on Electric Power Demand Forecasting

2021 ◽  
Author(s):  
Sandip Ashok Shivarkar ◽  
Sandeep Malik
Keyword(s):  
Power Generation ◽  
Electric Power ◽  
Smart Grids ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Power Demand ◽  
Current Development ◽  
Novel Approach ◽  
Power Generation Systems ◽  
Forecasting Techniques

Recently there has been tremendous change in use of the forecasting techniques due to the increase in availability of the power generation systems and the consumption of the electricity by different utilities. In the field of power generation and consumption it is important to have the accurate forecasting model to avoid the different losses. With the current development in the era of smart grids, it integrates electric power generation, demand and the storage, which requires more accurate and precise demand and generation forecasting techniques. This paper relates the most relevant studies on electric power demand forecasting, and presents the different models. This paper proposes a novel approach using machine learning for electric power demand forecasting.

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.

A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings

2014 ◽  
Vol 16 (3) ◽  
pp. 1460-1495 ◽  
Author(s):  
Luis Hernandez ◽  
Carlos Baladron ◽  
Javier M. Aguiar ◽  
Belen Carro ◽  
Antonio J. Sanchez-Esguevillas ◽  
...  
Keyword(s):  
Electric Power ◽  
Smart Grids ◽  
Demand Forecasting ◽  
Future Trends ◽  
Power Demand ◽  
Smart Buildings

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

A Combined Power Demand Forecasting Model with Variable Weight

2013 ◽  
Vol 392 ◽  
pp. 618-621
Author(s):  
Zhi Gang Wang ◽  
Qing Jie Zhou ◽  
Xing Hua Zhou
Keyword(s):  
Demand Forecasting ◽  
Development Trend ◽  
Forecasting Model ◽  
Power Demand ◽  
Proposed Model ◽  
Variable Weight ◽  
The Difference ◽  
Demand Forecasting Model ◽  
Analysis Models ◽  
The Impact

A combined power demand forecasting model with variable weight considering both of the impact of the macroeconomic situation and the internal development trend is proposed. The proposed model consists of regression analysis models and the trend extrapolation models. The variable weight is determined by the difference of the prediction results between the two kinds of models . Beijing's power demand forecasting illustrates the usefulness and reliability of the combined model.

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