Machine Leaming Applications for Load Predictions in Electrical Energy Network

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Nils Jakob Johannesen of the Faculty of Technology and Science at the University of Agder has submitted his thesis entitled «Machine Leaming Applications for Load Predictions in Electrical Energy Network» and will defend the thesis for the PhD-degree Monday 05 September 2022.

He has followed the PhD programme in Engineering and Science at UiA, with Specialisation in Engineering Sciences, scientific field Renewable Energy.

Read the summary of the thesis by Nils Jakob Johannesen:

Machine Leaming Applications for Load Predictions in Electrical Energy Network

Load forecasting is a required application in Smart-Grid, which provides essential input to other applications such as Demand Response, Topology Optimization and Anomaly Detection, facilitating the integration of intermittent clean energy sources.

The big data processing and operation of the energy system will require flexible tools to manage the smart energy system, by using Information and Communication Technologies, Distributed Generation and Artificial Intelligence, together.

Machine Learning can provide electrical load demand forecasting, giving information about future loads.

In the literature there are many methods on energy prediction, but most of them have used continuous time approach together with complex neural networks which requires huge amount of data.

Data of typical urban and rural energy network analysed

In this work collected operational data of typical urban and rural energy network are analysed for predictions of energy consumption, as well as for selected region of Nordpool electricity markets. The regression techniques are systematically investigated for electrical energy prediction and correlating other impacting parameters.

The k-Nearest Neighbour (kNN), Random Forest (RF) and Linear Regression (LR) are analysed and evaluated both by using continuous and vertical time approach.

It is observed that for 30 minutes predictions the RF Regression has the best results, shown by a mean absolute percentage error (MAPE) in the range of 1-2 %.

kNN show best results for the day-ahead forecasting with MAPE of 2.61 %.

The presented vertical time approach outperforms the continuous time approach.

Modelling

To enhance pre-processing stage, refined techniques from the domain of statistics and time series are adopted in the modelling.

Reducing the dimensionality through principal components analysis improves the predictive performance of Recurrent Neural Networks (RNN).

In the case of Gated Recurrent Units (GRU) networks, the results for all the seasons are improved through principal components analysis (PCA).

This work also considers abnormal operation due to various instances (e.g. random effect, intrusion, abnormal operation of smart devices, cyber-threats, etc.).

In the results of kNN, iforest and Local Outlier Factor (LOF) on urban area data and from rural region data, it is observed that the anomaly detection for the scenarios are different.

Different results for rural or urban area data

For the rural region, most of the anomalies are observed in the latter timeline of the data concentrated in the last year of the collected data.

For the urban area data, the anomalies are spread out over the entire timeline.

The frequency of detected anomalies were considerably higher for the rural area load demand than for the urban area load demand.

Observing from considered case scenarios, the incidents of detected anomalies are more data driven, than exceptions in the algorithms.

It is observed that from the domain knowledge of smart energy systems the LOF is able to detect observations that could not have detected by visual inspection alone, in contrast to kNN and iforest.

Whereas kNN and iforest excludes vii viii an upper and lower bound, the LOF is density based and separates out anomalies amidst in the data. The capability that LOF has to identify anomalies amidst the data together with the deep domain knowledge is an advantage, when detecting anomalies in smart meter data.

This work has shown that the instance based models can compete with models of higher complexity, yet some methods in preprocessing (such as circular coding) does not function for an instance based learner such as k-Nearest Neighbor, and hence kNN can not option for this kind of complexity even in the feature engineering of the model.

It will be interesting for the future work of electrical load forecasting to develop solution that combines a high complexity in the feature engineering and have the explainability of instance based models.

Disputation facts:

The trial lecture and the public defence will take place on campus in Auditorium C2 040, Campus Grimstad, and online via the Zoom conferencing app - registration link below.

Head of Department Paul Ragnar Svennevig, Department of Engineering Sciences, Faculty of Engineering and Science, University of Agder, will chair the disputation.

The trial lecture Monday 5 September at 10:15 hours

Public defense Monday 5 September at 12:15 hours

 

Given topic for trial lecture: «From Market Prediction to Load Forecasting in Power Distribution Networks and Smart Grids»

Thesis Title: «Machine Leaming Applications for Load Predictions in Electrical Energy Network»

Search for the thesis in AURA - Agder University Research Archive, a digital archive of scientific papers, theses and dissertations from the academic staff and students at the University of Agder.

The thesis is available here:

PHD_Thesis_NJ_Johannesen_UiA_FINAL_for_PRINT_with_ERRATA

 

The Candidate: Nils Jakob Johannesen (1975, Lørenskog) Bachelors degree as Electrical Engineer, UiA (2015), Masters degree in Renewable Energy, UiA (2018). Present position: Assistant Professor in Electric Power, University of South-Eastern Norway (USN).

Opponents:

First opponent: Associate Professor PhD Amin Hajizadeh, The Faculty of Engineering and Science, Aalborg University, Danmark

Second opponent: Associate Professor Amir Safari, Department of Science and Industry systems, University of South-Eastern Norway

Professor Frank Reichert, University of Agder,  is appointed as the administrator for the assessment committee.

Supervisors in the doctoral work were Professor Mohan Lal Kolhe, University of Agder (main supervisor) and Professor Morten Goodwin, University of Agder (co-supervisor)

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Opponent ex auditorio:

The chair invites members of the public to pose questions ex auditorio in the introduction to the public defense. Deadline is during the break between the two opponents. The person asking questions should have read the thesis. For online audience the Contact Persons e-mail are available in the chat function during the Public Defense, and questions ex auditorio can be submitted to Kristine Evensen Reinfjord at e-mail kristine.reinfjord@uia.no