Analysis of Ireland’s Electricity Usage

Lynch, Robert (2014) Analysis of Ireland’s Electricity Usage. Diploma thesis, Dublin, National College of Ireland.

PDF (Diploma)
Download (2MB) | Preview


This research follows a top-down approach into analysing Ireland’s electricity usage. The main objective was to give decision maker(s) a better understanding of the drivers of electricity consumption in Ireland, with associated information/statistics to back up these views. The analysis was produced using a combination of RStudio (2012), Weka (Hall et al., 2009) and Excel.

The main results were as follows:
- In overall terms maximum electricity demand was most highly correlated with daylight minutes, while minimum electricity demand was most highly correlated with heating degree days. However in relation to extreme cold periods the increase in maximum electricity demand was associated with the higher heating degree days.
- Daylight savings in the period showed an average reduction of weekday peak electricity demand of 177 MW when daylight savings ended and an average increase of 311 MW when daylight savings started.
- The overall electricity demand has a trough in the early morning while its peak moves monthly as it is affected by the length of the day (daylight minutes). The peak was around 6-7pm in the winter while being around 12-1pm in the summer.
- It was found that the modeling of the daily electricity demand should be split by the day of the week. Tuesday, Wednesday and Thursday could be modeled together with the other days of the week having their own models. Bank Holidays should be modeled like one of the weekend days.
- Linear Models and Decision Trees can model the maximum daily electricity demand accurately. This is due in part to the time series properties of the data. While time series visualisation was a good aid in identifying particular areas of interest.
- Wind generation is an unreliable source of electricity and it requires back-up capacity. In general the more wind the more back-up capacity that is required.

Item Type: Thesis (Diploma)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Divisions: School of Computing > Higher Diploma in Science in Data Analytics
Date Deposited: 16 Dec 2014 14:59
Last Modified: 16 Dec 2014 14:59

Actions (login required)

View Item View Item