Forecasting Energy Generation in Spain from Renewable Sources Using Time Series and Neural Network Models

Roshan Karthika, Saranya Varshni (2021) Forecasting Energy Generation in Spain from Renewable Sources Using Time Series and Neural Network Models. Masters thesis, Dublin, National College of Ireland.

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Abstract

Consumption of power has become an inevitable part of one’s being. With the constant development in economy and population, the energy demand never falls down instead it’s exponentially raising. In recent decades, the investments made in products and industrial sectors paves the path for 24/7 energy demand. It can be strongly stated that economic development in a country is highly proportional to energy generation. On a wide scale, power generation sources can be categorized as renewable energy sources (i.e.) solar, wind, hydro, geothermal, etc and fossil fuels which are non-renewable energy sources. Combustion of such non-renewable sources will lead to emission of toxic gases and CO2 into the environment leading to harmful consequences and these non-renewable sources get deprecate soon. So, it is safe to rely on the natural source of power generation and it is safe for the atmosphere as well. However, there is a lot of uncertainty factor associated with power generation from such environment-friendly sources. They are highly dependent on the climate and forecasting such challenging features could be worthy of working on. This energy generation forecasting will help many private and governmental organizations to develop a balance between the supply and demand chain. Hence, forecasting various energy sources using different neural network models and the ARIMA time series model will constitute a great research study as well as benefit the stakeholders from the power production sector and help them to be aware of the future trends before any investment plans.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electricity Supply H Social Sciences > HD Industries. Land use. Labor > Business Logistics > Supply Chain Management
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	10 Mar 2023 15:33
Last Modified:	10 Mar 2023 15:33
URI:	https://norma.ncirl.ie/id/eprint/6285

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