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Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems)ORCID iD: 0000-0001-6292-0695
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)ORCID iD: 0000-0002-9473-4536
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)ORCID iD: 0000-0003-0051-4098
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)ORCID iD: 0000-0003-4887-9547
2018 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 218, p. 159-172Article in journal (Refereed) Published
Abstract [en]

Probabilistic load forecasting (PLF) is of important value to grid operators, retail companies, demand response aggregators, customers, and electricity market bidders. Gaussian processes (GPs) appear to be one of the promising methods for providing probabilistic forecasts. In this paper, the log-normal process (LP) is newly introduced and compared to the conventional GP. The LP is especially designed for positive data like residential load forecasting—little regard was taken to address this issue previously. In this work, probabilisitic and deterministic error metrics were evaluated for the two methods. In addition, several kernels were compared. Each kernel encodes a different relationship between inputs. The results showed that the LP produced sharper forecasts compared with the conventional GP. Both methods produced comparable results to existing PLF methods in the literature. The LP could achieve as good mean absolute error (MAE), root mean square error (RMSE), prediction interval normalized average width (PINAW) and prediction interval coverage probability (PICP) as 2.4%, 4.5%, 13%, 82%, respectively evaluated on the normalized load data.

Place, publisher, year, edition, pages
2018. Vol. 218, p. 159-172
Keywords [en]
Gaussian process, Probabilistic load forecasting, Residential load
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Energy Systems
Research subject
Engineering Science with specialization in Science of Electricity
Identifiers
URN: urn:nbn:se:uu:diva-345282DOI: 10.1016/j.apenergy.2018.02.165ISI: 000430994500014OAI: oai:DiVA.org:uu-345282DiVA, id: diva2:1188936
Funder
Swedish Energy AgencyAvailable from: 2018-03-08 Created: 2018-03-08 Last updated: 2018-10-17Bibliographically approved
In thesis
1. Modeling and forecasting the load in the future electricity grid: Spatial electric vehicle load modeling and residential load forecasting
Open this publication in new window or tab >>Modeling and forecasting the load in the future electricity grid: Spatial electric vehicle load modeling and residential load forecasting
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The energy system is being transitioned to increase sustainability. This transition has been accelerated by the increased awareness about the adverse effects of the greenhouse gas (GHG) emissions into the atmosphere. The transition includes switching to electricity as the energy carrier in some sectors, e.g., transportation, increasing the contribution of renewable energy sources (RES) to the grid, and digitalizing the grid services.

Electric vehicles (EVs) are promoted and subsidized in many countries among the sustainability initiatives. Consequently, the global sales of EVs rapidly increased in the recent years. Many EV owners might charge their EVs only at home, thereby increasing the residential load. The residential load might further increase due to the initiatives to electrify the heating/cooling sector.

This thesis contributes to the knowledge about the operation of the future energy system by modeling the spatial charging load of private EVs in cities, and by proposing a forecasting model to predict the residential load. Both models can be used to evaluate the impacts of both technologies on the local electricity grid. In addition, demand response (DR) schemes can be proposed to reduce the adverse effects of both the charging load of EVs and the residential load.

A case study of the EV model on the Herrljunga city grid showed that 100% EV penetration with 3.7 kW (charging rate of 14.8 km/h) chargers will not cause voltage violations in the grid. Winter load is responsible for 5% voltage drop at the weakest bus, and EVs add only 1% to this drop. In a Swedish city, charging EVs will require adding extra 1.43 kW/car to the grid capacity—assuming 22 kW (charging rate of 88 km/h) residential chargers. If the EV charging is not restricted to residential locations, an increase of 1.23 kW/car is expected.

The proposed forecasting model is comparable in accuracy to previously developed models. As an advantage, the model produces a probability density function (PDF) describing the model’s certainty in the forecast. In contrast, many previous contributions provided only point forecasts.

Place, publisher, year, edition, pages
Uppsala universitet, 2018. p. 57
Keywords
Electric Vehicles, GIS, Photovoltaics, Residential Load Forecasting
National Category
Transport Systems and Logistics Infrastructure Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-359432 (URN)
Presentation
2018-10-22, Polhemsalen, Ångström laboratoriet, Lägerhyddsv. 1, 752 37, Uppsala, Sweden, 13:00 (English)
Opponent
Supervisors
Available from: 2018-09-27 Created: 2018-09-02 Last updated: 2018-09-27Bibliographically approved

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Shepero, Mahmoudvan der Meer, DennisMunkhammar, JoakimWidén, Joakim

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