Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

Le, Tuong; Vo, Minh Thanh; Vo, Bay; Hwang, Eenjun; Rho, Seungmin; Baik, Sung Wook

doi:10.3390/app9204237

Cited by 194 publications

(94 citation statements)

References 32 publications

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“…Note however that we employ 11 features instead of the 3 originally used by this work, and that we apply it for the 6 fault cases in our standardized testbed (Section III-B) instead of the 2 faults considered in [14]. The fifth method is the Bi-directional LSTM (or Bi-LSTM), which trains the data both from front to back and as well as from back to front [56]. The sixth and last method is the Multiresolution Signal Decomposition (MSD) of [13], with classification performed by SVM.…”

Section: Quantitative Evaluation a Methods Comparedmentioning

confidence: 99%

A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays

et al. 2020

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Fault diagnosis in photovoltaic (PV) arrays is essential in enhancing power output as well as the useful life span of a PV system. Severe faults such as Partial Shading (PS) and high impedance faults, low location mismatch, and the presence of Maximum Power Point Tracking (MPPT) make fault detection challenging in harsh environmental conditions. In this regard, there have been several attempts made by various researchers to identify PV array faults. However, most of the previous work has focused on fault detection and classification in only a few faulty scenarios. This paper presents a novel approach that utilizes deep two-dimensional (2-D) Convolutional Neural Networks (CNN) to extract features from 2-D scalograms generated from PV system data in order to effectively detect and classify PV system faults. An in-depth quantitative evaluation of the proposed approach is presented and compared with previous classification methods for PV array faults-both classical machine learning based and deep learning based. Unlike contemporary work, five different faulty cases (including faults in PS-on which no work has been done before in the machine learning domain) have been considered in our study, along with the incorporation of MPPT. We generate a consistent dataset over which to compare ours and previous approaches, to make for the first (to the best of our knowledge) comprehensive and meaningful comparative evaluation of fault diagnosis. It is observed that the proposed method involving fine-tuned pre-trained CNN outperforms existing techniques, achieving a high fault detection accuracy of 73.53%. Our study also highlights the importance of representative and discriminative features to classify faults (as opposed to the use of raw data), especially in the noisy scenario, where our method achieves the best performance of 70.45%. We believe that our work will serve to guide future research in PV system fault diagnosis. INDEX TERMS Photovoltaic array, maximum power point tracking, fault classification, convolutional neural network, scalograms, transfer learning.

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Section: Quantitative Evaluation a Methods Comparedmentioning

confidence: 99%

A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays

et al. 2020

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“…So far, various single algorithm-based STLF models have been proposed [46]. Even though they showed good performance in the domains that were focused on, their performance was limited in the other domains or electric energy consumption patterns were intricate.…”

Section: Two-stage Stlf Model Constructionmentioning

confidence: 99%

A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling

et al. 2020

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Smart grid systems, which have gained much attention due to its ability to reduce operation and management costs of power systems, consist of diverse components including energy storage, renewable energy, and combined cooling, heating and power (CCHP) systems. The CCHP has been investigated to reduce energy costs by using the thermal energy generated during the power generation process. For efficient utilization of CCHP and numerous power generation systems, accurate short-term load forecasting (STLF) is necessary. So far, even though many single algorithm-based STLF models have been proposed, they showed limited success in terms of applicability and coverage. This problem can be alleviated by combining such single algorithm-based models in ways that take advantage of their strengths. In this paper, we propose a novel two-stage STLF scheme; extreme gradient boosting and random forest models are executed in the first stage, and deep neural networks are executed in the second stage to combine them. To show the effectiveness of our proposed scheme, we compare our model with other popular single algorithm-based forecasting models and then show how much electric charges can be saved by operating CCHP based on the schedules made by the economic analysis on the predicted electric loads.

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“…To construct an STLF model using only "hour" as an input (independent) variable, we used one statistical technique and two machine learning algorithms. Even though SVM, DL, and boosting methods exhibit excellent prediction performance in STLF [7][8][9][34][35][36][37], they require a significant amount of time to optimize the various hyperparameters and also require sufficient data sets. We did not consider these methods because we constructed an STLF model using a data set from the building electric energy consumption data of only 24 h. Thus, we considered MLR, DT, and RF, which allow simple model construction and exhibit satisfactory prediction performance [38,39].…”

Section: Case 1: Time Factor-based Forecasting Modelmentioning

confidence: 99%

Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods

et al. 2020

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An energy-management system requires accurate prediction of the electric load for optimal energy management. However, if the amount of electric load data is insufficient, it is challenging to perform an accurate prediction. To address this issue, we propose a novel electric load forecasting scheme using the electric load data of diverse buildings. We first divide the electric energy consumption data into training and test sets. Then, we construct multivariate random forest (MRF)-based forecasting models according to each building except the target building in the training set and a random forest (RF)-based forecasting model using the limited electric load data of the target building in the test set. In the test set, we compare the electric load of the target building with that of other buildings to select the MRF model that is the most similar to the target building. Then, we predict the electric load of the target building using its input variables via the selected MRF model. We combine the MRF and RF models by considering the different electric load patterns on weekdays and holidays. Experimental results demonstrate that combining the two models can achieve satisfactory prediction performance even if the electric data of only one day are available for the target building.

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Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

Cited by 194 publications

References 32 publications

A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays

A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays

A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling

Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods

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