Prediction of Changes in Electrical Power Consumption in future with the help of ARIMA model, with other Machine and Deep Learning Model

-, Syed Mohammad Shahrukh (2021) Prediction of Changes in Electrical Power Consumption in future with the help of ARIMA model, with other Machine and Deep Learning Model. Masters thesis, Dublin, National College of Ireland.

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Abstract

If we see the role of the atmosphere in changing the electricity demand, it plays an immense role. There is an indirect relationship between the demand for electricity load and atmospheric pressure. Also, the change in atmospheric pressure plays a vital role in climate change and global warming, and due to this rise in atmospheric pressure, the earth temperature is exponentially increasing. Nowadays, we can observe that we are getting hotter summers and colder winters every passing year than before. Due to this, the domestic and industrial electricity demand continuously increases, creating enormous challenges for the electricity providing companies and present governments. So, as a result, anticipating future electrical energy requirements based on changes in atmospheric pressure, will be highly beneficial to both the government and the electrical supply firms. Various studies on how to compete with this electrical energy requirement issue have been conducted in the past. In this study, we will look at how atmospheric pressure affects the demand for electrical energy indirectly. Here, we have implemented the (KDD) machine learning method and models like SARIMAX, ANN(LSTM), BiLSTM, linear regression model, Lasso Model, KNN model, Random Forest and ARIMA, which are also used to anticipate future values. We will use these tactics to see if we will get superior accuracy values for our model. The electrical energy industry and government authorities can always use these models to improve their planning and prepare their electrical generating plants for future requirements.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Electricity load, Atmospheric Pressure, SARIMAX, ANN(LSTM), BiLSTM; linear regression model; Lasso Model; KNN model; Random Forest and ARIMA
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 T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electricity Supply
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Clara Chan
Date Deposited:	15 Dec 2021 10:06
Last Modified:	15 Dec 2021 10:06
URI:	https://norma.ncirl.ie/id/eprint/5228

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