Regulatory Framework for Long-Term Demand Forecasting and Power Procurement Planning

2019 ◽  
Author(s):  
Anoop Singh ◽  
Manvendra Pratap ◽  
Abhishek Das ◽  
Piyush A. Sharma ◽  
Kamal K. Gupta
Keyword(s):  
Demand Forecasting ◽  
Regulatory Framework ◽  
Procurement Planning

scholarly journals Nonlinear ARIMAX model for long –term sectoral demand forecasting

2018 ◽  
pp. 581-592 ◽  
Author(s):  
Najmeh Neshat ◽  
Hengameh Hadian ◽  
Matineh Behzad
Keyword(s):  
Demand Forecasting

scholarly journals Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method

2021 ◽  
Vol 11 (18) ◽  
pp. 8612
Author(s):  
Santanu Kumar Dash ◽  
Michele Roccotelli ◽  
Rasmi Ranjan Khansama ◽  
Maria Pia Fanti ◽  
Agostino Marcello Mangini
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Sudden Change ◽  
Electricity Demand ◽  
Gradient Boosting ◽  
Forecasting Model ◽  
Demand Forecast ◽  
Daily Consumption ◽  
Energy Provider

The long-term electricity demand forecast of the consumer utilization is essential for the energy provider to analyze the future demand and for the accurate management of demand response. Forecasting the consumer electricity demand with efficient and accurate strategies will help the energy provider to optimally plan generation points, such as solar and wind, and produce energy accordingly to reduce the rate of depletion. Various demand forecasting models have been developed and implemented in the literature. However, an efficient and accurate forecasting model is required to study the daily consumption of the consumers from their historical data and forecast the necessary energy demand from the consumer’s side. The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows one to reduce the demand for electricity by studying the daily usage pattern of consumers, which would significantly help to cope with the accurate evaluation. The efficiency of the proposed forecasting model is compared with various conventional models. In addition, by the utilization of power consumption data, power theft detection in the distribution line is monitored to avoid financial losses by the utility provider. This paper also deals with the consumer’s energy analysis, useful in tracking the data consistency to detect any kind of abnormal and sudden change in the meter reading, thereby distinguishing the tampering of meters and power theft. Indeed, power theft is an important issue to be addressed particularly in developing and economically lagging countries, such as India. The results obtained by the proposed methodology have been analyzed and discussed to validate their efficacy.

Long Term Demand Forecasting System for Demand Driven Manufacturing

2021 ◽  
pp. 13-20
Author(s):  
Sleiman Rita ◽  
Tran Kim-Phuc ◽  
Thomassey Sébastien
Keyword(s):  
Demand Forecasting ◽  
Forecasting System

A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting

Energy ◽  
2020 ◽  
Vol 204 ◽  
pp. 117948 ◽  
Author(s):  
Mohammad-Rasool Kazemzadeh ◽  
Ali Amjadian ◽  
Turaj Amraee
Keyword(s):  
Data Mining ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Peak Load ◽  
Energy Demand Forecasting ◽  
Hybrid Data
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