A Novel Evolutionary-Based Deep Convolutional Neural Network Model for Intelligent Load Forecasting

Jalali, Seyed Mohammad Jafar; Ahmadian, Sajad; Khosravi, Abbas; Shafie‐khah, Miadreza; Nahavandi, Saeid; Catalào, João P. S.

doi:10.1109/tii.2021.3065718

Cited by 148 publications

(51 citation statements)

References 42 publications

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“…However, the EC load data contains long-term periodicity and fluctuations, which limit the performance of MLR in some cases. Similarly, in [4], an enhanced deep learning architecture is proposed for short term electricity load forecasting. In the proposed work, a deep CNN is built for extracting the complex non-linear patterns from the historical EC load profile and providing an optimal short-term electricity load forecast.…”

Section: Related Workmentioning

confidence: 99%

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Electricity Consumption Forecasting Using Gated-FCN With Ensemble Strategy

Naz

Javaid²,

Asif

et al. 2021

IEEE Access

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Accurate electricity consumption forecasting in the power grids ensures efficient generation and distribution of electricity. Keeping this in mind, the paper introduces a novel deep learning model, termed Gated-FCN, for short-term load forecasting. The key idea is to introduce an automated feature selection and deep learning model for forecasting. The model includes an eight-layered Fully Convolutional Network (FCN-8) in which the hand-crafted feature selection that requires expert domain knowledge is avoided. Furthermore, the model also reduces noise as it learns internal dependencies and the correlation of the time series. Enhanced Bidirectional Gated Recurrent Unit (EBiGRU) is used in combination with FCN-8 to learn long-term temporal dependencies of the time series. Moreover, a weighted averaging mechanism of multiple snapshot models is adopted in the proposed model to assign optimized weights to BiGRU. At the end of FCN-8 and BiGRU, a fully connected dense layer is used that gives final prediction results. Gated-FCN is an end-to-end forecasting model that does not require any other model for enhancing its forecasting efficiency. Different activation functions are initially analyzed to determine how the proposed model learns complex patterns from the time series data. Later, the activation function having the best accuracy is used for forecasting. The proposed model extracts both spatial and temporal features from the data. Furthermore, this paper also provides predictive and exploratory data analyses to assist policymakers in making optimal decisions regarding power production and dispatch. In order to demonstrate the applicability of the proposed technique, the simulations are performed using nine years' load consumption data taken from Independent System Operators New England (ISO-NE). The comparison with five state-of-the-art techniques is also provided to prove the fact that Gated-FCN gives the best forecasting accuracy as compared to other benchmark techniques in terms of two performance metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).

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Section: Related Workmentioning

confidence: 99%

“…It does not capture the temporal correlated features from the long-term electricity load profile properly. Furthermore, the study of [4] presents an enhanced CNN by utilizing the neuroevolution algorithm as a hyperparameter tuning algorithm. However, the standalone CNN does not accurately forecast the electricity load profile.…”

Section: Introductionmentioning

confidence: 99%

Electricity Consumption Forecasting Using Gated-FCN With Ensemble Strategy

Naz

Javaid²,

Asif

et al. 2021

IEEE Access

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“…Several traditional and modern evolutionary algorithms in the recent years are proposed in which their famous ones are regarded as ant colony [49] , as well as gray wolf optimizer [50] , fruit fly optimization algorithm [51] , moth–flame optimizer [52] and Harris hawks optimization [53] which have been effectively used in a wide range of applications [54] . Deep neuroevolution is an efficient concept that is based on designing the architecture of deep neural networks optimally and automatically based on the robust evolutionary computational algorithms [27] , [55] , [56] . On the other hand, ensemble techniques can increase the performance capability of the classifier by integrating multiple classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

An oppositional-Cauchy based GSK evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for COVID-19 diagnosis

Jalali

Ahmadian

et al. 2021

Applied Soft Computing

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A novel coronavirus (COVID-19) has globally attracted attention as a severe respiratory condition. The epidemic has been first tracked in Wuhan, China, and has progressively been expanded in the entire world. The growing expansion of COVID-19 around the globe has made X-ray images crucial for accelerated diagnostics. Therefore, an effective computerized system must be established as a matter of urgency, to facilitate health care professionals in recognizing X-ray images from COVID-19 patients. In this work, we design a novel artificial intelligent-based automated X-ray image analysis framework based on an ensemble of deep optimized convolutional neural networks (CNNs) in order to distinguish coronavirus patients from non-patients. By developing a modified version of gaining–sharing knowledge (GSK) optimization algorithm using the Opposition-based learning (OBL) and Cauchy mutation operators, the architectures of the deployed deep CNNs are optimized automatically without performing the general trial and error procedures. After obtaining the optimized CNNs, it is also very critical to identify how to decrease the number of ensemble deep CNN classifiers to ensure the classification effectiveness. To this end, a selective ensemble approach is proposed for COVID-19 X-ray based image classification using a deep Q network that combines reinforcement learning (RL) with the optimized CNNs. This approach increases the model performance in particular and therefore decreases the ensemble size of classifiers. The experimental results show that the proposed deep RL optimized ensemble approach has an excellent performance over two popular X-ray image based COVID-19 datasets. Our proposed advanced algorithm can accurately identify the COVID-19 patients from the normal individuals with a significant accuracy of 0.991441, precision of 0.993568, recall (sensitivity) of 0.981445, F-measure of 0.989666 and AUC of 0.990337 for Kaggle dataset as well as an excellent accuracy of 0.987742, precision of 0.984334, recall (sensitivity) of 0.989123, F-measure of 0.984939 and AUC of 0.988466 for Mendely dataset.

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“…Unlike the grid search and random search approaches, the Bayesian approach considers past evaluation results while choosing a new hyperparameter set. But it is still hard to implement the Bayesian approach in practice [17].…”

Section: Introductionmentioning

confidence: 99%

Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks

Bataineh

Kaur

2021

IEEE Access

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This paper aims to automatically design optimal LSTM topologies using the clonal selection algorithm (CSA) to solve text classification tasks such as sentiment analysis and SMS spam classification. Designing optimal topologies involves determining the best configuration of hyperparameters that will give the best performance. The current state-of-the-art LSTM topologies are often designed using trial and error approaches which are incredibly time-consuming and require domain experts. Our proposed method, referred to as CSA-LSTM, is evaluated using the Large Movie Review Dataset (IMDB). Furthermore, to verify the robustness of the hyperparameters discovered by CSA for the IMDB dataset, we have used them for the other datasets, viz. the Twitter US Airline Sentiment and the SMS Spam Collection. Additionally, the discovered hyperparameters for the LSTM are combined with pre-determined convolutional neural network (CNN) layers to achieve the same or better results to fast the training time and fewer trainable parameters. For further verification and evaluation of the generalization ability and effectiveness of the proposed approach, it is compared with four machine learning algorithms widely used for text classification tasks: (1) random forest (RF), (2) logistic regression (LR), (3) support vector machine (SVM), and (4) multinomial naive Bayes (NB). The results of our experiments show that the LSTM topologies automatically designed by our CSA method are less expensive, reusable and outperform the machine learning algorithms and other models in the literature evaluated on the same three datasets. Through our proposed method, LSTM's best topology can be self-determined without any human intervention, making CSA-based algorithms a promising approach to automatically design optimal LSTM topologies that provide the best performance for a given task.INDEX TERMS clonal selection algorithm, deep learning, hyperparameters optimization, IMDB, immunocomputing, long short-term memory.

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A Novel Evolutionary-Based Deep Convolutional Neural Network Model for Intelligent Load Forecasting

Cited by 148 publications

References 42 publications

Electricity Consumption Forecasting Using Gated-FCN With Ensemble Strategy

Electricity Consumption Forecasting Using Gated-FCN With Ensemble Strategy

An oppositional-Cauchy based GSK evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for COVID-19 diagnosis

Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks

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