Microgrid Multivariate Load Forecasting Based on Weighted Visibility Graph: A Regional Airport Case Study
This paper introduces an alternative forecasting approach that leverages the application of visibility graphs in the context of multivariate energy forecasting for a regional airport, which incorporates energy demand of diverse types of buildings and wind power generation. The motivation for this re...
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MDPI AG
2025-04-01
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| Series: | Electricity |
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| Online Access: | https://www.mdpi.com/2673-4826/6/2/17 |
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| author | Georgios Vontzos Vasileios Laitsos Dimitrios Bargiotas Athanasios Fevgas Aspassia Daskalopulu Lefteri H. Tsoukalas |
| author_facet | Georgios Vontzos Vasileios Laitsos Dimitrios Bargiotas Athanasios Fevgas Aspassia Daskalopulu Lefteri H. Tsoukalas |
| author_sort | Georgios Vontzos |
| collection | DOAJ |
| description | This paper introduces an alternative forecasting approach that leverages the application of visibility graphs in the context of multivariate energy forecasting for a regional airport, which incorporates energy demand of diverse types of buildings and wind power generation. The motivation for this research stems from the urgent need to enhance the accuracy and reliability of load forecasting in microgrids, which is crucial for optimizing energy management, integrating renewable sources, and reducing operational costs, thereby contributing to more sustainable and efficient energy systems. The proposed methodology employs visibility graph transformations, the superposed random walk method, and temporal decay adjustments, where more recent observations are weighted more significantly to predict the next time step in the data set. The results indicate that the proposed method exhibits satisfactory performance relative to comparison models such as Exponential smoothing, ARIMA, Light Gradient Boosting Machine and CNN-LSTM. The proposed method shows improved performance in forecasting energy consumption for both stationary and highly variable time series, with SMAPE and NMRSE values typically in the range of 4–10% and 5–20%, respectively, and an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> reaching 0.96. The proposed method affords notable benefits to the forecasting of energy demand, offering a versatile tool for various kinds of structures and types of energy production in a microgrid. This study lays the groundwork for further research and real-world applications within this field by enhancing both the theoretical and practical aspects of time series forecasting, including load forecasting. |
| format | Article |
| id | doaj-art-dd42787682be41b6b3511863cf2e5186 |
| institution | Kabale University |
| issn | 2673-4826 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electricity |
| spelling | doaj-art-dd42787682be41b6b3511863cf2e51862025-08-20T03:27:01ZengMDPI AGElectricity2673-48262025-04-01621710.3390/electricity6020017Microgrid Multivariate Load Forecasting Based on Weighted Visibility Graph: A Regional Airport Case StudyGeorgios Vontzos0Vasileios Laitsos1Dimitrios Bargiotas2Athanasios Fevgas3Aspassia Daskalopulu4Lefteri H. Tsoukalas5Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, GreeceCenter for Intelligent Energy Systems (CiENS), School of Nuclear Engineering, Purdue University, West Lafayette, IN 47906, USAThis paper introduces an alternative forecasting approach that leverages the application of visibility graphs in the context of multivariate energy forecasting for a regional airport, which incorporates energy demand of diverse types of buildings and wind power generation. The motivation for this research stems from the urgent need to enhance the accuracy and reliability of load forecasting in microgrids, which is crucial for optimizing energy management, integrating renewable sources, and reducing operational costs, thereby contributing to more sustainable and efficient energy systems. The proposed methodology employs visibility graph transformations, the superposed random walk method, and temporal decay adjustments, where more recent observations are weighted more significantly to predict the next time step in the data set. The results indicate that the proposed method exhibits satisfactory performance relative to comparison models such as Exponential smoothing, ARIMA, Light Gradient Boosting Machine and CNN-LSTM. The proposed method shows improved performance in forecasting energy consumption for both stationary and highly variable time series, with SMAPE and NMRSE values typically in the range of 4–10% and 5–20%, respectively, and an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> reaching 0.96. The proposed method affords notable benefits to the forecasting of energy demand, offering a versatile tool for various kinds of structures and types of energy production in a microgrid. This study lays the groundwork for further research and real-world applications within this field by enhancing both the theoretical and practical aspects of time series forecasting, including load forecasting.https://www.mdpi.com/2673-4826/6/2/17microgridvisibility graphsmultivariate forecastingairports |
| spellingShingle | Georgios Vontzos Vasileios Laitsos Dimitrios Bargiotas Athanasios Fevgas Aspassia Daskalopulu Lefteri H. Tsoukalas Microgrid Multivariate Load Forecasting Based on Weighted Visibility Graph: A Regional Airport Case Study Electricity microgrid visibility graphs multivariate forecasting airports |
| title | Microgrid Multivariate Load Forecasting Based on Weighted Visibility Graph: A Regional Airport Case Study |
| title_full | Microgrid Multivariate Load Forecasting Based on Weighted Visibility Graph: A Regional Airport Case Study |
| title_fullStr | Microgrid Multivariate Load Forecasting Based on Weighted Visibility Graph: A Regional Airport Case Study |
| title_full_unstemmed | Microgrid Multivariate Load Forecasting Based on Weighted Visibility Graph: A Regional Airport Case Study |
| title_short | Microgrid Multivariate Load Forecasting Based on Weighted Visibility Graph: A Regional Airport Case Study |
| title_sort | microgrid multivariate load forecasting based on weighted visibility graph a regional airport case study |
| topic | microgrid visibility graphs multivariate forecasting airports |
| url | https://www.mdpi.com/2673-4826/6/2/17 |
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