Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method
نویسندگان
چکیده
The long-term electricity demand forecast of the consumer utilization is essential for energy provider to analyze future and accurate management response. Forecasting with efficient strategies will help optimally plan generation points, such as solar wind, produce accordingly reduce rate depletion. Various forecasting models have been developed implemented in literature. However, an model required study daily consumption consumers from their historical data necessary consumer’s side. proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) technique allows one by studying usage pattern consumers, which would significantly cope evaluation. efficiency compared various conventional models. In addition, power data, theft detection distribution line monitored avoid financial losses utility provider. This paper also deals analysis, useful tracking consistency detect any kind abnormal sudden change meter reading, thereby distinguishing tampering meters theft. Indeed, important issue be addressed particularly developing economically lagging countries, India. results obtained methodology analyzed discussed validate efficacy.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2021
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app11188612