Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder

Kim, Jin Young; Cho, Sung-Bae

doi:10.3390/en12040739

Cited by 113 publications

(51 citation statements)

References 23 publications

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“…The method proposed in [28] used a hybrid approach combining a CNN with LSTM to predict residential power consumption, and obtained values of 0.3738 and 0.6114 for MSE and RMSE, respectively. We also compare our method with one proposed by Kim et al [55], who used an auto-encoder-based deep learning model to forecast energy demand and used a backpropagation through time algorithm to train their forecasting time series model. They also evaluated their method using MSE, obtaining values of 0.3840 and 0.3953 for the MSE and MAE metrics, respectively.…”

Section: F Comparative Analysis Of Our Proposed Methodsmentioning

confidence: 99%

“…A total of nine variables with their respective units are shown, and these form the power consumption data. We also give quantitative details of the power consumption dataset in Table IV given in the literature [55]. From Table IV, we can see that the maximum active power consumed is 11.122 Kilowatts, while the minimum active power is 0.076 Kilowatts.…”

Section: A Individual Household Power Consumption Datasetmentioning

confidence: 99%

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Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks

et al. 2020

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Excessive power consumption (PC) and demand for power is increasing on a daily basis, due to advancements in technology, the rise in electricity-dependent machinery, and the growth of the human population. It has become necessary to predict PC in order to improve power management and cooperation between the energy used in a building and the power grid. State-of-the-art energy consumption prediction (ECP) methods are limited in terms of predicting the energy effectively, due to various challenges such as weather conditions and the dynamic behaviour of occupants. Thus, to overcome the drawbacks of these methods, we present an intelligent hybrid technique that combines a convolutional neural network (CNN) with a multi-layer bi-directional long-short term memory (M-BLSTM) method using three steps. When applied to short-term power ECP, this approach helps to provide efficient power management i.e. it can assist the supplier to produce the optimum amount of power. The first step in our proposed method integrates the pre-processing and data organisation mechanisms to refine the data and remove abnormalities. The second step employs a deep learning network, where the sequence of refined data is fed into the CNN via the M-BLSTM network to learn the sequence pattern effectively. The third step generates the ECP/PC by comparing actual and predicted data series and evaluates the prediction using error metrics. The proposed method achieves better prediction results than existing techniques, thus demonstrating its effectiveness. Furthermore, it achieved the smallest value of the Mean Square Error (MSE) and Root Mean Square Error (RMSE) for individual household dataset using 10-fold cross validation (CV) and a hold-out (CV) method. INDEX TERMS Artificial intelligence, deep learning, power consumption, CNN, bi-directional LSTM, and short-term energy consumption.

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Section: F Comparative Analysis Of Our Proposed Methodsmentioning

confidence: 99%

Section: A Individual Household Power Consumption Datasetmentioning

confidence: 99%

Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks

et al. 2020

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“…In this section, the performance of the proposed CNN-GRU model over IHEPC dataset is compared with baseline models. The results are compared with, linear regression [49] SVM [54], CNN-LSTM [48], autoencoder [49], multilayer bidirectional LSTM (MLBD_LSTM) [14] and deep neural network (DNN) [50] as shown in Figure 6. For instance, linear regression attained 0.…”

Section: F Comparison Of Proposed Cnn-gru Model Over Ihepc Dataset Wmentioning

confidence: 99%

A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting

et al. 2020

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Electric energy forecasting domain attracts researchers due to its key role in saving energy resources, where mainstream existing models are based on Gradient Boosting Regression, Artificial Neural Networks, Extreme Learning Machine and Support Vector Machine. These models encounter high-level of non-linearity between input data and output predictions and limited adoptability in real-world scenarios. Meanwhile, energy forecasting domain demands more robustness, higher prediction accuracy and generalization ability for real-world implementation. In this paper, we achieve the mentioned tasks by developing a hybrid sequential learning-based energy forecasting model that employs Convolution Neural Network and Gated Recurrent Units into a unified framework for accurate energy consumption prediction. The proposed framework has two major phases: (1) data refinement and (2) training, where the data refinement phase applies preprocessing strategies over raw data. In the training phase, CNN features are extracted from input dataset and fed in to GRU, that is selected as optimal and observed to have enhanced sequence learning abilities after extensive experiments. The proposed model is an effective alternative to the previous hybrid models in terms of computational complexity as well prediction accuracy, due to the representative features' extraction potentials of CNNs and effectual gated structure of multi-layered GRU. The experimental evaluation over existing energy forecasting datasets reveal the better performance of our method in terms of preciseness and efficiency. The proposed method achieved the smallest error rate on individual Appliances Energy prediction and household electric power consumption datasets, when compared to other baseline models.

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“…(J.-Y. Kim and Cho, 2019) proposed a technique dependent on deep learning that comprises of a projector for characterizing a proper state for a given circumstance and an indicator that conjectures vitality request from the characterized state. Results gave better execution (MSE = 0.384) than the ordinary models.…”

Section: Related Workmentioning

confidence: 99%

Forecasting IRAN’s max daily demand for electricity in different weather types using deep learning approach

Abdoli¹,

Mehrara²,

Ardalani³

2019

IJET

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Electricity network management is a complicated issue that needs a forecast of consumption. This predication is generally hard and a challenging topic due to nonlinearity and permanent fluctuation of load in the network. Although linearities of time-series load may give an estimation, linearization of nonlinear data to foresee removes the nature of information which shows anomalies in electricity demand. New approaches of (ANN) help to make a forecast model with keeping information properties. In this paper, we categorized max daily load data in IRAN based on climate zone to figure the following 2 months. Long Short-Term Memory (LSTM) from deep learning approach picks and yields contrasted with different steps of epochs. The outcomes appear epochs do not necessarily improve the accuracy of prediction and best MADP for cold, tropical and temperament zone, are 1.26%, 2.5%, and 10.49%, respectively.

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Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder

Cited by 113 publications

References 23 publications

Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks

Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks

A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting

Forecasting IRAN’s max daily demand for electricity in different weather types using deep learning approach

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