A Metaheuristics-Based Inputs Selection and Training Set Formation Method for Load Forecasting
Load forecasting is a procedure of fundamental importance in power systems operation and planning. Many entities can benefit from accurate load forecasting such as generation companies, systems operators, retailers, prosumers, and others. A variety of models have been proposed so far in the literatu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/8/1733 |
| _version_ | 1797431714949103616 |
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| author | Ioannis Panapakidis Michail Katsivelakis Dimitrios Bargiotas |
| author_facet | Ioannis Panapakidis Michail Katsivelakis Dimitrios Bargiotas |
| author_sort | Ioannis Panapakidis |
| collection | DOAJ |
| description | Load forecasting is a procedure of fundamental importance in power systems operation and planning. Many entities can benefit from accurate load forecasting such as generation companies, systems operators, retailers, prosumers, and others. A variety of models have been proposed so far in the literature. Among them, artificial neural networks are a favorable approach mainly due to their potential for capturing the relationship between load and other parameters. The forecasting performance highly depends on the number and types of inputs. The present paper presents a particle swarm optimization (PSO) two-step method for increasing the performance of short-term load forecasting (STLF). During the first step, PSO is applied to derive the optimal types of inputs for a neural network. Next, PSO is applied again so that the available training data is split into homogeneous clusters. For each cluster, a different neural network is utilized. Experimental results verify the robustness of the proposed approach in a bus load forecasting problem. Also, the proposed algorithm is checked on a load profiling problem where it outperforms the most common algorithms of the load profiling-related literature. During input selection, the weights update is held in asymmetrical duration. The weights of the training phase require more time compared with the test phase. |
| first_indexed | 2024-03-09T09:49:18Z |
| format | Article |
| id | doaj.art-bfe65041985c4245a098a2e7c46c8706 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T09:49:18Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-bfe65041985c4245a098a2e7c46c87062023-12-02T00:22:20ZengMDPI AGSymmetry2073-89942022-08-01148173310.3390/sym14081733A Metaheuristics-Based Inputs Selection and Training Set Formation Method for Load ForecastingIoannis Panapakidis0Michail Katsivelakis1Dimitrios Bargiotas2Department of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, GreeceLoad forecasting is a procedure of fundamental importance in power systems operation and planning. Many entities can benefit from accurate load forecasting such as generation companies, systems operators, retailers, prosumers, and others. A variety of models have been proposed so far in the literature. Among them, artificial neural networks are a favorable approach mainly due to their potential for capturing the relationship between load and other parameters. The forecasting performance highly depends on the number and types of inputs. The present paper presents a particle swarm optimization (PSO) two-step method for increasing the performance of short-term load forecasting (STLF). During the first step, PSO is applied to derive the optimal types of inputs for a neural network. Next, PSO is applied again so that the available training data is split into homogeneous clusters. For each cluster, a different neural network is utilized. Experimental results verify the robustness of the proposed approach in a bus load forecasting problem. Also, the proposed algorithm is checked on a load profiling problem where it outperforms the most common algorithms of the load profiling-related literature. During input selection, the weights update is held in asymmetrical duration. The weights of the training phase require more time compared with the test phase.https://www.mdpi.com/2073-8994/14/8/1733clusteringload forecastingmetaheuristicsneural networksparticle swarm optimization |
| spellingShingle | Ioannis Panapakidis Michail Katsivelakis Dimitrios Bargiotas A Metaheuristics-Based Inputs Selection and Training Set Formation Method for Load Forecasting Symmetry clustering load forecasting metaheuristics neural networks particle swarm optimization |
| title | A Metaheuristics-Based Inputs Selection and Training Set Formation Method for Load Forecasting |
| title_full | A Metaheuristics-Based Inputs Selection and Training Set Formation Method for Load Forecasting |
| title_fullStr | A Metaheuristics-Based Inputs Selection and Training Set Formation Method for Load Forecasting |
| title_full_unstemmed | A Metaheuristics-Based Inputs Selection and Training Set Formation Method for Load Forecasting |
| title_short | A Metaheuristics-Based Inputs Selection and Training Set Formation Method for Load Forecasting |
| title_sort | metaheuristics based inputs selection and training set formation method for load forecasting |
| topic | clustering load forecasting metaheuristics neural networks particle swarm optimization |
| url | https://www.mdpi.com/2073-8994/14/8/1733 |
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