An adaptive hybrid fractal model for short-term load forecasting in power systems
•Short-term load forecasting (STLF) of power loads involves fractal features, due to complexity of power systems.•Composite linear fractal interpolation function (CLFIF) modeling better captures irregularities and fluctuations of power loads.•Iterative learning (IL) and chimp optimization algorithm...
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| Vydáno v: | Electric power systems research Ročník 207; s. 107858 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
01.06.2022
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0378-7796, 1873-2046 |
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| Abstract | •Short-term load forecasting (STLF) of power loads involves fractal features, due to complexity of power systems.•Composite linear fractal interpolation function (CLFIF) modeling better captures irregularities and fluctuations of power loads.•Iterative learning (IL) and chimp optimization algorithm (ChOA) are used for adaptive parametrization of CLFIF.•Hybrid CLFIF-IL-ChOA modeling is proposed for STLF, which obtains high accuracy without further relevant data.•Comparisons in between CLFIF-IL-ChOA and others are included; effectiveness of CLFIF-IL-ChOA is verified.
This paper develops a hybrid short-term load forecasting (STLF) modeling with adaptive parametrization, based on composite linear fractal interpolation function (CLFIF), iterative learning (IL) and chimp optimization algorithm (ChOA). More precisely, after selecting similar days in power load data, an amendatory CLFIF model is constructed for an-hour-ahead prediction in terms of hourly load curves. Then, iterative learning based on ChOA optimizes the parameters of the amendatory CLFIF model with higher accuracy. Moreover, to confirm effectiveness of the CLFIF-IL-ChOA model, numerical examples are tested on the historical power load data from PJM and ENTSOE. The numerical results show that the proposed method can obtain higher accuracy, compared to some common methods about time series analysis. |
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| AbstractList | •Short-term load forecasting (STLF) of power loads involves fractal features, due to complexity of power systems.•Composite linear fractal interpolation function (CLFIF) modeling better captures irregularities and fluctuations of power loads.•Iterative learning (IL) and chimp optimization algorithm (ChOA) are used for adaptive parametrization of CLFIF.•Hybrid CLFIF-IL-ChOA modeling is proposed for STLF, which obtains high accuracy without further relevant data.•Comparisons in between CLFIF-IL-ChOA and others are included; effectiveness of CLFIF-IL-ChOA is verified.
This paper develops a hybrid short-term load forecasting (STLF) modeling with adaptive parametrization, based on composite linear fractal interpolation function (CLFIF), iterative learning (IL) and chimp optimization algorithm (ChOA). More precisely, after selecting similar days in power load data, an amendatory CLFIF model is constructed for an-hour-ahead prediction in terms of hourly load curves. Then, iterative learning based on ChOA optimizes the parameters of the amendatory CLFIF model with higher accuracy. Moreover, to confirm effectiveness of the CLFIF-IL-ChOA model, numerical examples are tested on the historical power load data from PJM and ENTSOE. The numerical results show that the proposed method can obtain higher accuracy, compared to some common methods about time series analysis. This paper develops a hybrid short-term load forecasting (STLF) modeling with adaptive parametrization, based on composite linear fractal interpolation function (CLFIF), iterative learning (IL) and chimp optimization algorithm (ChOA). More precisely, after selecting similar days in power load data, an amendatory CLFIF model is constructed for an-hour-ahead prediction in terms of hourly load curves. Then, iterative learning based on ChOA optimizes the parameters of the amendatory CLFIF model with higher accuracy. Moreover, to confirm effectiveness of the CLFIF-IL-ChOA model, numerical examples are tested on the historical power load data from PJM and ENTSOE. The numerical results show that the proposed method can obtain higher accuracy, compared to some common methods about time series analysis. |
| ArticleNumber | 107858 |
| Author | Li, Xiaolan Zhou, Jun |
| Author_xml | – sequence: 1 givenname: Xiaolan surname: Li fullname: Li, Xiaolan email: lixiaolan@hhu.edu.cn – sequence: 2 givenname: Jun surname: Zhou fullname: Zhou, Jun email: zhouj@lzu.edu.cn |
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| Keywords | Chimp optimization algorithm Adaptive Composite linear fractal interpolation function Short-term load forecasting Iterative learning |
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| Snippet | •Short-term load forecasting (STLF) of power loads involves fractal features, due to complexity of power systems.•Composite linear fractal interpolation... This paper develops a hybrid short-term load forecasting (STLF) modeling with adaptive parametrization, based on composite linear fractal interpolation... |
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| SubjectTerms | Adaptive Algorithms Chimp optimization algorithm Composite linear fractal interpolation function Electricity distribution Forecasting Fractal models Fractals Interpolation Iterative learning Iterative methods Machine learning Model accuracy Numerical analysis Optimization Optimization algorithms Parameterization Short-term load forecasting Time series |
| Title | An adaptive hybrid fractal model for short-term load forecasting in power systems |
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