Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Towards short term electricity load forecasting using improved support vector machine and extreme learning machine
AU - Ahmad, W.
AU - Ayub, N.
AU - Ali, T.
AU - Irfan, M.
AU - Awais, M.
AU - Shiraz, M.
AU - Glowacz, A.
PY - 2020/6/5
Y1 - 2020/6/5
N2 - Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier's hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques. © 2020 by the authors.
AB - Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier's hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques. © 2020 by the authors.
KW - Electricity load forecasting
KW - Extreme Learning Machine
KW - Feature selection
KW - Genetic Algorithm
KW - Grid Search
KW - Smart grid
KW - Support Vector Machine
KW - Deep learning
KW - Electric load forecasting
KW - Electric power plant loads
KW - Energy utilization
KW - Feature extraction
KW - Forecasting
KW - Genetic algorithms
KW - Heuristic algorithms
KW - Knowledge acquisition
KW - Learning algorithms
KW - Support vector machines
KW - Extreme learning machine
KW - Feature extraction techniques
KW - Machine learning techniques
KW - Meta heuristic algorithm
KW - Optimization algorithms
KW - Recursive feature elimination
KW - Short-term electricity load forecasting
KW - Learning systems
U2 - 10.3390/en13112907
DO - 10.3390/en13112907
M3 - Journal article
VL - 13
JO - Energies
JF - Energies
SN - 1996-1073
IS - 11
M1 - 2907
ER -