Estimation of Load Profiles for Secondary Substations
(2020) In CODEN:LUTEDX/TEIE EIEM01 20201Industrial Electrical Engineering and Automation
- Abstract
- The power system today is facing a transformation from fossil and nuclear energy sources to renewable energy sources such as solar- and wind power. At the same time electric vehicles are becoming more common every year. To analyse how these two changes affect the power system it is crucial to understand how loaded the power system is today from traditional loads on a local and regional level. The purpose of this thesis has been to create the best possible representation of today’s electricity consumption. This is done by generalising and aggregating hourly collected samples of load from energy meters used at low voltage customers of Kraftringen in Lund. The estimation is done on medium voltage level substations by collecting publicly... (More)
- The power system today is facing a transformation from fossil and nuclear energy sources to renewable energy sources such as solar- and wind power. At the same time electric vehicles are becoming more common every year. To analyse how these two changes affect the power system it is crucial to understand how loaded the power system is today from traditional loads on a local and regional level. The purpose of this thesis has been to create the best possible representation of today’s electricity consumption. This is done by generalising and aggregating hourly collected samples of load from energy meters used at low voltage customers of Kraftringen in Lund. The estimation is done on medium voltage level substations by collecting publicly available features which include information about the expected electricity consumption. The resulting model has combined the strengths of linear regression and artificial feed forward neural networks. The model has a mean absolute percentage error of only 10% when evaluated on unseen data from stations used in training the model and 16% when evaluated on an entirely unseen station. The model has been compared to different implementations of the load curve method (typkurvor) which it outperforms with a mean absolute error 54% smaller than the best load curve implementation. The results are based on data from residential districts only and therefore the accuracy of the methods are limited to residential districts. One of the major advantages of this model is that it should be able to predict the electricity consumption from unseen residential districts with only feature data, no data of the electricity consumption from unseen areas is needed. Besides modelling hourly values of load from substations, an extreme value theory model has been used to model the expected maximum loads that occur for one station. This is done with a combination of block maxima of two week-period blocks and by using the Generalised Extreme Value distribution. The resulting model covers all observed load maxima when including confidence intervals of 95% and can be used to predict the expected maximum load for a given time period. The models shown in this thesis can be used by researchers and utility companies to generate expected load of substations and also to model extreme values of load. (Less)
- Popular Abstract (Swedish)
- Behovet av att estimera elförbrukningen timme för timme under årets alla dagar har funnits länge hos elnätsbolag och forskare, framför allt på nätstationsnivå. Många metoder som används i Sverige idag är dock gamla och använder endast kundernas årsförbrukning som indata under hela året trots att det blir allt vanligare med nätbolag som mäter energiförbrukningen varje timme hos samtliga kunder. Med maskininlärning och statistik kan uppmätta timvärden utnyttjas för att få mer noggranna modeller att estimera elförbrukningen med. Dessa modeller kan sedan användas för att uppskatta elförbrukningen för andra stationer.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9030028
- author
- Nilsson, Hans-Christian LU
- supervisor
- organization
- alternative title
- Generalising AMR Data with a Statistics and Deep Learning Approach
- course
- EIEM01 20201
- year
- 2020
- type
- H3 - Professional qualifications (4 Years - )
- subject
- keywords
- Load Estimation, Synthetic Load, Feed Forward Neural Networks, Extreme Value Theory, Block Maxima, Linear Regression, Load Curves, Electricity Consumption Estimation
- publication/series
- CODEN:LUTEDX/TEIE
- report number
- CODEN:LUTEDX/(TEIE-5447)/1-39/(2020)
- language
- English
- id
- 9030028
- date added to LUP
- 2020-10-22 09:30:51
- date last changed
- 2020-12-09 16:52:18
@misc{9030028,
abstract = {{The power system today is facing a transformation from fossil and nuclear energy sources to renewable energy sources such as solar- and wind power. At the same time electric vehicles are becoming more common every year. To analyse how these two changes affect the power system it is crucial to understand how loaded the power system is today from traditional loads on a local and regional level. The purpose of this thesis has been to create the best possible representation of today’s electricity consumption. This is done by generalising and aggregating hourly collected samples of load from energy meters used at low voltage customers of Kraftringen in Lund. The estimation is done on medium voltage level substations by collecting publicly available features which include information about the expected electricity consumption. The resulting model has combined the strengths of linear regression and artificial feed forward neural networks. The model has a mean absolute percentage error of only 10% when evaluated on unseen data from stations used in training the model and 16% when evaluated on an entirely unseen station. The model has been compared to different implementations of the load curve method (typkurvor) which it outperforms with a mean absolute error 54% smaller than the best load curve implementation. The results are based on data from residential districts only and therefore the accuracy of the methods are limited to residential districts. One of the major advantages of this model is that it should be able to predict the electricity consumption from unseen residential districts with only feature data, no data of the electricity consumption from unseen areas is needed. Besides modelling hourly values of load from substations, an extreme value theory model has been used to model the expected maximum loads that occur for one station. This is done with a combination of block maxima of two week-period blocks and by using the Generalised Extreme Value distribution. The resulting model covers all observed load maxima when including confidence intervals of 95% and can be used to predict the expected maximum load for a given time period. The models shown in this thesis can be used by researchers and utility companies to generate expected load of substations and also to model extreme values of load.}},
author = {{Nilsson, Hans-Christian}},
language = {{eng}},
note = {{Student Paper}},
series = {{CODEN:LUTEDX/TEIE}},
title = {{Estimation of Load Profiles for Secondary Substations}},
year = {{2020}},
}