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Ismail, Zuhaimy
- An Empirical Mode Decomposition Approach to Peak Load Demand Forecasting
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Authors
Affiliations
1 Department of Mathematical Sciences, Universiti of Teknologi Malaysia, Johor Bahru, Johor, MY
1 Department of Mathematical Sciences, Universiti of Teknologi Malaysia, Johor Bahru, Johor, MY
Source
Indian Journal of Science and Technology, Vol 6, No 9 (2013), Pagination: 5201-5207Abstract
An accurate and reliable electric load forecasting model is very essential for efficient and effective operation of the Electricity Supply Industry (ESI). Several single models have been developed for electric load forecast for ESI but it is becoming increasingly difficult to obtain accurate forecast by these models because of the volatility coupled with the nonlinear and non- stationary nature of electric load series. In this paper, we propose a novel Electric Peak load forecasting model that combines empirical mode decomposition (EMD) and artificial neural network (ANN). The propose model involves three stages of development. In the first stage, the historical load data obtained from Power holding company of Nigeria (PHCN), Bida is decomposed into several intrinsic mode functions and a residue component using the EMD sifting process. The second stage involves building separate neural network models for each of these IMFS and residue component and the last stage involves combining the predictions from these models and making forecast. When the forecast from this model is compared with that obtained from a conventional neural network model, it was observed that the proposed model outperforms the conventional neural network model, by 2.3% for the whole year model and by 1.8% for the weekday model, judging by the forecast accuracy of both models.Keywords
Empirical Mode Decomposition, Intrinsic Mode Function, Sifting, Model, ForecastingFull Text
References
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- New Approach to Peak Load Forecasting based on EMD and ANFIS
Abstract Views :345 |
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Authors
Affiliations
1 Deparment of Mathematical Sciences, Universiti of Teknologi, MY
1 Deparment of Mathematical Sciences, Universiti of Teknologi, MY
Source
Indian Journal of Science and Technology, Vol 6, No 12 (2013), Pagination: 5600–5606Abstract
This paper presents a new approach to Peak Load Forecasting based on EMD and ANFIS. EMD is used to decompose the load data into several Intrinsic Mode Functions (IMFs); then appropriate ANFIS models are developed for these IMFs. The target model proposed in this paper, EMD–ANFIS, is achieved by combining the predictions from these IMF–ANFIS models together and this is used for forecasting purposes. A real life data obtained from Power Holding Company of Nigeria (PHCN), Bida, was used to evaluate the forecast accuracy of the proposed model. The results revealed that the proposed EMD–ANFIS model yields better results when compared to ANN and EMD–ANN models. The proposed EMD–ANFIS model recorded 2.76% and 50.05% improvements over EMD–ANN and traditional ANN models, respectively as judged by the overall MAPE of the models.Keywords
EMD, IMF, ANFIS, Forecasting Accuracy, Peak LoadFull Text
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- A Hybrid GA-FEEMD for Forecasting Crude Oil Prices
Abstract Views :180 |
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Authors
Affiliations
1 Faculty of Sciences, Department of Mathematical Sciences, Universiti Teknologi Malaysia, 81310, Skudai, Johor, MY
1 Faculty of Sciences, Department of Mathematical Sciences, Universiti Teknologi Malaysia, 81310, Skudai, Johor, MY
Source
Indian Journal of Science and Technology, Vol 10, No 31 (2017), Pagination:Abstract
Forecasting crude oil prices is essential but usually involves a difficult process. In this paper, we proposed a hybrid Genetic Algorithm and Fast Ensemble Empirical Mode Decomposition (GA-FEEMD) for forecasting crude oil price time series data. The proposed GA-FEEMD basically involves three steps. Firstly, we decomposed the original crude oil price time series data into two Intrinsic Mode Functions (IMFs) using FEEMD algorithm. Then, we forecasted the second IMF which basically is the intrinsic trend of the crude oil prices. Then, we applied Genetic Algorithm (GA) to obtain the stopping criterion in the FEEMD process. The hybrid GA-FEEMD forecasting model was compared with ARIMA and Artificial Neural Network methods. The results showed that the proposed GA-FEEMD model improved the forecasting accuracy of the crude oil price time series data.Keywords
Crude Oil Price Forecast, Fast Ensemble Empirical Mode Decomposition (FEEMD), Genetic Algorithm (GA) and HybridizationFull Text