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Forecasting Residential Electricity Load Demand using Machine Learning

Panigrahi, Arun (2020) Forecasting Residential Electricity Load Demand using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

Since the emergence of different forms of sophisticated home appliances and smart home devices globally, the demand for residential energy is rapidly growing. This growing demand has led to create energy sustainability issues, which have been identified as one of the major concerns in the recent times as more consumers need more energy. Therefore, the forecasting of demand for electricity plays a crucial role in maintaining an equilibrium between the consumers demand and energy generated by the energy producing companies. In recent years, numerous machine learning algorithms have been employed to forecast the consumers electricity load demand. This study has been carried out as a comparative analysis of four different machine learning algorithms, Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX), Seasonal Autoregressive Integrated Moving Average with Explanatory Variable (SARIMAX), PROPHET and Long-Short Term Memory (LSTM) for a group of residential customer present at London. It is observed that the efficiency of the LSTM model is considerably greater than all the other implemented models.
Keywords - Residential Energy, Forecasting, Electricity, Energy Demand, ARIMAX, SARIMAX, PROPHET, LSTM

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 25 Jan 2021 13:26
Last Modified: 25 Jan 2021 13:26
URI: http://norma.ncirl.ie/id/eprint/4460

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