Forecasting day, week and month ahead electricity load consumption of a building using empirical mode decomposition and extreme learning machine

Khan, Sajjad; Javaid, Nadeem; Chand, Annas; Abbasi, Raza Abid; Khan, Abdul Basit Majeed; Faisal, Hafiz Muhammad

doi:10.1109/iwcmc.2019.8766675

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…They can be classified into two groups of methods. The first group relates to using statistical methods, such as multiple regression, exponential smoothing, and autoregressive integrated moving average (ARIMA) [6][7]. The second group employs artificial intelligence techniques, such as support vector machines (SVM) and artificial neural networks (ANNs) [8][9].…”

Section: Introductionmentioning

confidence: 99%

Effects of Data Standardization on Hyperparameter Optimization with the Grid Search Algorithm Based on Deep Learning: A Case Study of Electric Load Forecasting

Ngoc

Đại

Minh

2022

Adv. technol. innov.

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This study investigates data standardization methods based on the grid search (GS) algorithm for energy load forecasting, including zero-mean, min-max, max, decimal, sigmoid, softmax, median, and robust, to determine the hyperparameters of deep learning (DL) models. The considered DL models are the convolutional neural network (CNN) and long short-term memory network (LSTMN). The procedure is made over (i) setting the configuration for CNN and LSTMN, (ii) establishing the hyperparameter values of CNN and LSTMN models based on epoch, batch, optimizer, dropout, filters, and kernel, (iii) using eight data standardization methods to standardize the input data, and (iv) using the GS algorithm to search the optimal hyperparameters based on the mean absolute error (MAE) and mean absolute percent error (MAPE) indexes. The effectiveness of the proposed method is verified on the power load data of the Australian state of Queensland and Vietnamese Ho Chi Minh city. The simulation results show that the proposed data standardization methods are appropriate, except for the zero-mean and min-max methods.

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Section: Introductionmentioning

confidence: 99%

Effects of Data Standardization on Hyperparameter Optimization with the Grid Search Algorithm Based on Deep Learning: A Case Study of Electric Load Forecasting

Ngoc

Đại

Minh

2022

Adv. technol. innov.

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“…and statistical methods (Multiple Regression, Exponential Smoothing, ARIMA and Seasonal ARIMA, etc.) [5,6]. Recent developments in artificial neural networks, especially Deep Learn-ing (DL) neural networks, have been becoming one of the most active technologies in load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

Tran

2020

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The Convolutional Neural Network (CNN) model is one of the most effective models for load forecasting with hyperparameters which can be used not only to determine the CNN structure and but also to train the CNN model. This paper proposes a framework for Grid Search hyperparameters of the CNN model. In a training process, the optimal models will specify conditions that satisfy requirement for minimum of accuracy scores of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). In the testing process, these optimal models will be used to evaluate the results along with all other ones. The results indicated that the optimal models have accuracy scores near the minimum values. Load demand data of Queensland (Australia) and Ho Chi Minh City (Vietnam) were utilized to verify the accuracy and reliability of the Grid Search framework.

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“…Calculated daily MAPE and compared with other models. Sajjad Khan et al[13] presented "empirical mode decomposition based extreme learning machine" technique for daily, weekly and monthly building load forecasting and results compared with CNN model. Hossein Javedani Sadaei et al[14] presented a method using fuzzy time series and convolutional neural networks to forecast STL Dagdougui H.et al[15] Proposed NN model using Bayesian and Levenberg learning algorithms to forecast short-term load in buildings.…”

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confidence: 99%

Electrical Load Forecasting using ARIMA, Prophet and LSTM Networks

Ananthu

K²

2021

IJEER

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Forecasting electrical load plays a vital role in power system planning. However, it is quite difficult to forecast electrical load, as the load on the system varies continuously concerning time and seasons. In this paper, we are proposing an advanced artificial neural network model to forecast short-term electrical load. The proposed method tested on historical data collected from Karnataka power corporation, India, and test results compared with other data-driven models viz. ARIMA, RNN, LSTM, and Prophet. The accuracy and RMSE values were calculated and observed that the proposed model was superior in a day and weekly ahead electrical load forecasting.

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Forecasting day, week and month ahead electricity load consumption of a building using empirical mode decomposition and extreme learning machine

Cited by 6 publications

References 18 publications

Effects of Data Standardization on Hyperparameter Optimization with the Grid Search Algorithm Based on Deep Learning: A Case Study of Electric Load Forecasting

Effects of Data Standardization on Hyperparameter Optimization with the Grid Search Algorithm Based on Deep Learning: A Case Study of Electric Load Forecasting

Archives of Electrical Engineering

Electrical Load Forecasting using ARIMA, Prophet and LSTM Networks

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