Dynamic Thermal Rating Forecasting Methods: A Systematic Survey
Dynamic Thermal Rating (DTR) allows optimum electric power line rating use. It is an intelligent grid technology predicting changes in line rating due to changing physical and environmental conditions. This study performed a meta-analysis of DTR forecasting methods by classifying the methods, implem...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9797691/ |
| _version_ | 1811333909436170240 |
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| author | Olatunji Ahmed Lawal Jiashen Teh |
| author_facet | Olatunji Ahmed Lawal Jiashen Teh |
| author_sort | Olatunji Ahmed Lawal |
| collection | DOAJ |
| description | Dynamic Thermal Rating (DTR) allows optimum electric power line rating use. It is an intelligent grid technology predicting changes in line rating due to changing physical and environmental conditions. This study performed a meta-analysis of DTR forecasting methods by classifying the methods, implementing them, and comparing their outputs for a 24hr forecast lead time. It implemented deep learning methods of Recurrent Neural Network (RNN), Ensemble Means forecasting and Convolution Neural Network (CNN). RNN uses the initial outcome of a specific neural network layer as feedback to the network to predict the layer’s outcome. Ensemble Means forecasting is a Monte-Carlo simulation process producing random, equally viable forecasting solutions. On the other hand, CNN uses unsupervised learning to predict features with minimal errors. This survey systematically implements Quantile Regression (QR), RNN, CNN and Ensemble means forecasting. Point error metrics and probabilistic error metrics of sharpness, skill, and bias were used in the methods’ evaluation. All methods tested prove to be efficient, but 50th percentile QR appears more conservative, secure and less error-prone. It achieved between 35% - 45% line capacity utilization over the Static Thermal Rating (STR). On average, judging by the error metrics of all methods, 50th percentile quantile regression proves highly reliable and provides a better conviction in our choice of DTR forecasting. |
| first_indexed | 2024-04-13T16:59:54Z |
| format | Article |
| id | doaj.art-c47a607010394984958c04af80e3f967 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T16:59:54Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c47a607010394984958c04af80e3f9672022-12-22T02:38:42ZengIEEEIEEE Access2169-35362022-01-0110651936520510.1109/ACCESS.2022.31836069797691Dynamic Thermal Rating Forecasting Methods: A Systematic SurveyOlatunji Ahmed Lawal0https://orcid.org/0000-0002-6875-6504Jiashen Teh1https://orcid.org/0000-0001-9741-6245School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), Nibong Tebal, Penang, MalaysiaSchool of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), Nibong Tebal, Penang, MalaysiaDynamic Thermal Rating (DTR) allows optimum electric power line rating use. It is an intelligent grid technology predicting changes in line rating due to changing physical and environmental conditions. This study performed a meta-analysis of DTR forecasting methods by classifying the methods, implementing them, and comparing their outputs for a 24hr forecast lead time. It implemented deep learning methods of Recurrent Neural Network (RNN), Ensemble Means forecasting and Convolution Neural Network (CNN). RNN uses the initial outcome of a specific neural network layer as feedback to the network to predict the layer’s outcome. Ensemble Means forecasting is a Monte-Carlo simulation process producing random, equally viable forecasting solutions. On the other hand, CNN uses unsupervised learning to predict features with minimal errors. This survey systematically implements Quantile Regression (QR), RNN, CNN and Ensemble means forecasting. Point error metrics and probabilistic error metrics of sharpness, skill, and bias were used in the methods’ evaluation. All methods tested prove to be efficient, but 50th percentile QR appears more conservative, secure and less error-prone. It achieved between 35% - 45% line capacity utilization over the Static Thermal Rating (STR). On average, judging by the error metrics of all methods, 50th percentile quantile regression proves highly reliable and provides a better conviction in our choice of DTR forecasting.https://ieeexplore.ieee.org/document/9797691/Dynamic thermal ratingsmart gridsstochastic forecastsdeep learning forecastspoint forecast errorsprobabilistic forecast errors |
| spellingShingle | Olatunji Ahmed Lawal Jiashen Teh Dynamic Thermal Rating Forecasting Methods: A Systematic Survey IEEE Access Dynamic thermal rating smart grids stochastic forecasts deep learning forecasts point forecast errors probabilistic forecast errors |
| title | Dynamic Thermal Rating Forecasting Methods: A Systematic Survey |
| title_full | Dynamic Thermal Rating Forecasting Methods: A Systematic Survey |
| title_fullStr | Dynamic Thermal Rating Forecasting Methods: A Systematic Survey |
| title_full_unstemmed | Dynamic Thermal Rating Forecasting Methods: A Systematic Survey |
| title_short | Dynamic Thermal Rating Forecasting Methods: A Systematic Survey |
| title_sort | dynamic thermal rating forecasting methods a systematic survey |
| topic | Dynamic thermal rating smart grids stochastic forecasts deep learning forecasts point forecast errors probabilistic forecast errors |
| url | https://ieeexplore.ieee.org/document/9797691/ |
| work_keys_str_mv | AT olatunjiahmedlawal dynamicthermalratingforecastingmethodsasystematicsurvey AT jiashenteh dynamicthermalratingforecastingmethodsasystematicsurvey |