Abstract

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.

Highlights

  • With the current increase in global warming, the focus of energy dependency has moved towards renewable energy sources (RESs), which seemingly have zero emission of greenhouse gases

  • Storage devices are essential to optimize the use of renewable energy by storing energy when available and supplying it to consumers according to their requirement

  • Autoregressive integrated moving average (ARIMA) in the proposed model to improve the forecast performance with respect to existing prediction models

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Summary

Introduction

With the current increase in global warming, the focus of energy dependency has moved towards renewable energy sources (RESs), which seemingly have zero emission of greenhouse gases. Many researchers have focused on various power measuring devices [6,7,8] These literature studies point out that the dynamic consumer electricity requirement has become a challenge for the power grid and sudden inflation in power demand and future energy requirement by the various categories of load cannot be efficiently predicted. To overcome this limitation, many forecasting techniques to be applied by using smart metering and control systems are proposed [9,10].

Related Works
Demand Forecasting Models and Power Theft Detection Strategy
ARIMA Model
Support Vector Regression Forecasting Model
5: Calculate
Power Theft Detection Algorithm
Implementation of Energy Forecasting and Power Theft Models
Forecast
Comparative Analysis Based on Error Indices Calculations
Findings
Conclusions
Full Text
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