Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method
The efficient spatial load forecasting (SLF) is of high interest for the planning of power distribution networks, mainly in areas with high rates of urbanization. The ever-present spatial error of SLF arises the need for probabilistic assessment of the long-term point forecasts. This paper introduce...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/18/4643 |
| _version_ | 1827706711112155136 |
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| author | Vasileios Evangelopoulos Panagiotis Karafotis Pavlos Georgilakis |
| author_facet | Vasileios Evangelopoulos Panagiotis Karafotis Pavlos Georgilakis |
| author_sort | Vasileios Evangelopoulos |
| collection | DOAJ |
| description | The efficient spatial load forecasting (SLF) is of high interest for the planning of power distribution networks, mainly in areas with high rates of urbanization. The ever-present spatial error of SLF arises the need for probabilistic assessment of the long-term point forecasts. This paper introduces a probabilistic SLF framework with prediction intervals, which is based on a hierarchical trending method. More specifically, the proposed hierarchical trending method predicts the magnitude of future electric loads, while the planners’ knowledge is used to improve the allocation of future electric loads, as well as to define the year of introduction of new loads. Subsequently, the spatial error is calculated by means of root-mean-squared error along the service territory, based on which the construction of the prediction intervals of the probabilistic forecasting part takes place. The proposed probabilistic SLF is introduced to serve as a decision-making tool for regional planners and distribution network operators. The proposed method is tested on a real-world distribution network located in the region of Attica, Athens, Greece. The findings prove that the proposed method shows high spatial accuracy and reduces the spatial error compared to a business-as-usual approach. |
| first_indexed | 2024-03-10T16:30:21Z |
| format | Article |
| id | doaj.art-0c28411341404781b9563c8946e154d2 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T16:30:21Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0c28411341404781b9563c8946e154d22023-11-20T12:51:16ZengMDPI AGEnergies1996-10732020-09-011318464310.3390/en13184643Probabilistic Spatial Load Forecasting Based on Hierarchical Trending MethodVasileios Evangelopoulos0Panagiotis Karafotis1Pavlos Georgilakis2School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, GreeceSchool of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, GreeceSchool of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, GreeceThe efficient spatial load forecasting (SLF) is of high interest for the planning of power distribution networks, mainly in areas with high rates of urbanization. The ever-present spatial error of SLF arises the need for probabilistic assessment of the long-term point forecasts. This paper introduces a probabilistic SLF framework with prediction intervals, which is based on a hierarchical trending method. More specifically, the proposed hierarchical trending method predicts the magnitude of future electric loads, while the planners’ knowledge is used to improve the allocation of future electric loads, as well as to define the year of introduction of new loads. Subsequently, the spatial error is calculated by means of root-mean-squared error along the service territory, based on which the construction of the prediction intervals of the probabilistic forecasting part takes place. The proposed probabilistic SLF is introduced to serve as a decision-making tool for regional planners and distribution network operators. The proposed method is tested on a real-world distribution network located in the region of Attica, Athens, Greece. The findings prove that the proposed method shows high spatial accuracy and reduces the spatial error compared to a business-as-usual approach.https://www.mdpi.com/1996-1073/13/18/4643distribution networkshierarchical trending methodprediction intervalprobabilistic forecastingspatial load forecasting |
| spellingShingle | Vasileios Evangelopoulos Panagiotis Karafotis Pavlos Georgilakis Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method Energies distribution networks hierarchical trending method prediction interval probabilistic forecasting spatial load forecasting |
| title | Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method |
| title_full | Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method |
| title_fullStr | Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method |
| title_full_unstemmed | Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method |
| title_short | Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method |
| title_sort | probabilistic spatial load forecasting based on hierarchical trending method |
| topic | distribution networks hierarchical trending method prediction interval probabilistic forecasting spatial load forecasting |
| url | https://www.mdpi.com/1996-1073/13/18/4643 |
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