Assessment of Electric Consumption Forecast Using Machine Learning and Deep Learning Models for the Industrial Sector

2022 ◽  
pp. 206-218
Author(s):  
Bhawna Dhupia ◽  
M. Usha Rani
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Short Term Memory ◽  
Rapid Development ◽  
Demand Forecasting ◽  
Industrial Sector ◽  
Power Demand ◽  
Learning Models ◽  
Energy Forecasting ◽  
Evaluation Metric

Power demand forecasting is one of the fields which is gaining popularity for researchers. Although machine learning models are being used for prediction in various fields, they need to upgrade to increase accuracy and stability. With the rapid development of AI technology, deep learning (DL) is being recommended by many authors in their studies. The core objective of the chapter is to employ the smart meter's data for energy forecasting in the industrial sector. In this chapter, the author will be implementing popular power demand forecasting models from machine learning and compare the results of the best-fitted machine learning (ML) model with a deep learning model, long short-term memory based on RNN (LSTM-RNN). RNN model has vanishing gradient issue, which slows down the training in the early layers of the network. LSTM-RNN is the advanced model which take care of vanishing gradient problem. The performance evaluation metric to compare the superiority of the model will be R2, mean square error (MSE), root means square error (RMSE), and mean absolute error (MAE).

scholarly journals A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 374
Author(s):  
Babacar Gaye ◽  
Dezheng Zhang ◽  
Aziguli Wulamu
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
State Of The Art ◽  
Accuracy Score ◽  
Learning Models ◽  
Proposed Model

With the extensive availability of social media platforms, Twitter has become a significant tool for the acquisition of peoples’ views, opinions, attitudes, and emotions towards certain entities. Within this frame of reference, sentiment analysis of tweets has become one of the most fascinating research areas in the field of natural language processing. A variety of techniques have been devised for sentiment analysis, but there is still room for improvement where the accuracy and efficacy of the system are concerned. This study proposes a novel approach that exploits the advantages of the lexical dictionary, machine learning, and deep learning classifiers. We classified the tweets based on the sentiments extracted by TextBlob using a stacked ensemble of three long short-term memory (LSTM) as base classifiers and logistic regression (LR) as a meta classifier. The proposed model proved to be effective and time-saving since it does not require feature extraction, as LSTM extracts features without any human intervention. We also compared our proposed approach with conventional machine learning models such as logistic regression, AdaBoost, and random forest. We also included state-of-the-art deep learning models in comparison with the proposed model. Experiments were conducted on the sentiment140 dataset and were evaluated in terms of accuracy, precision, recall, and F1 Score. Empirical results showed that our proposed approach manifested state-of-the-art results by achieving an accuracy score of 99%.

scholarly journals A Machine Learning Gateway for Scientific Workflow Design

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Brian Broll ◽  
Umesh Timalsina ◽  
Péter Völgyesi ◽  
Tamás Budavári ◽  
Ákos Lédeczi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Rapid Development ◽  
Scientific Workflow ◽  
Version Control ◽  
Learning Models ◽  
Remote Execution ◽  
Collaboration Support ◽  
Ease Of Access ◽  
Easy Integration

The paper introduces DeepForge, a gateway to deep learning for scientific computing. DeepForge provides an easy to use, yet powerful visual/textual interface to facilitate the rapid development of deep learning models by novices as well as experts. Utilizing a cloud-based infrastructure, built-in version control, and multiuser collaboration support, DeepForge promotes reproducibility and ease of access and enables remote execution of machine learning pipelines. The tool currently supports TensorFlow/Keras, but its extensible architecture enables easy integration of additional platforms.

scholarly journals Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability

2020 ◽  
Vol 12 (3) ◽  
pp. 1109 ◽  
Author(s):  
Choi ◽  
Cho ◽  
Kim
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Demand Management ◽  
Demand Forecasting ◽  
Sudden Change ◽  
Residential Building ◽  
Error Rates ◽  
Seasonal Forecasting ◽  
Power Demand ◽  
Short Term

The purpose of this study is to design a novel custom power demand forecasting algorithm based on the LSTM Deep-Learning method regarding the recent power demand patterns. We performed tests to verify the error rates of the forecasting module, and to confirm the sudden change of power patterns in the actual power demand monitoring system. We collected the power usage data in every five-minute resolution in a day from some groups of the residential, public offices, hospitals, and industrial factories buildings in one year. In order to grasp the external factors and to predict the power demand of each facility, a comparative experiment was conducted in three ways; short-term, long-term, seasonal forecasting exp[eriments. The seasonal patterns of power demand usages were analyzed regarding the residential building. The overall error rates of power demand forecasting using the proposed LSTM module were reduced in terms of each facility. The predicted power demand data shows a certain pattern according to each facility. Especially, the forecasting difference of the residential seasonal forecasting pattern in summer and winter was very different from other seasons. It is possible to reduce unnecessary demand management costs by the designed accurate forecasting method.

scholarly journals A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting

2020 ◽  
Vol 10 (23) ◽  
pp. 8491
Author(s):  
Bin Liu ◽  
Zhexi Zhang ◽  
Junchi Yan ◽  
Ning Zhang ◽  
Hongyuan Zha ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Short Term Memory ◽  
Critical Time ◽  
Learning Approach ◽  
Learning Models ◽  
Comparison Results ◽  
Online Lending ◽  
Machine Learning Models

Risk control has always been a major challenge in finance. Overdue repayment is a frequently encountered discreditable behavior in online lending. Motivated by the powerful capabilities of deep neural networks, we propose a fusion deep learning approach, namely AD-MBLSTM, based on the deep neural network (DNN), multi-layer bi-directional long short-term memory (LSTM) (BiLSTM) and the attention mechanism for overdue repayment behavior forecasting according to historical repayment records. Furthermore, we present a novel feature derivation and selection method for the procedure of data preprocessing. Visualization and interpretability improvement work is also implemented to explore the critical time points and causes of overdue repayment behavior. In addition, we present a new dataset originating from a practical application scenario in online lending. We evaluate our proposed framework on the dataset and compare the performance with various general machine learning models and neural network models. Comparison results and the ablation study demonstrate that our proposed model outperforms many effective general machine learning models by a large margin, and each indispensable sub-component takes an active role.

scholarly journals Research on power demand forecasting based on attention mechanism and deep learning network

2022 ◽  
Vol 355 ◽  
pp. 02022
Author(s):  
Chenglong Zhang ◽  
Li Yao ◽  
Jinjin Zhang ◽  
Junyong Wu ◽  
Baoguo Shan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Attention Mechanism ◽  
Power Demand ◽  
Short Term ◽  
Time Step ◽  
Long Short Term Memory ◽  
Deep Learning Network ◽  
Deep Learning Model

Combining actual conditions, power demand forecasting is affected by various uncertain factors such as meteorological factors, economic factors, and diversity of forecasting models, which increase the complexity of forecasting. In response to this problem, taking into account that different time step states will have different effects on the output, the attention mechanism is introduced into the method proposed in this paper, which improves the deep learning model. Improved models of convolutional neural networks (CNN) and long short-term memory (LSTM) that combine the attention mechanism are proposed respectively. Finally, according to the verification results of actual examples, it is proved that the proposed method can obtain a smaller error and the prediction performance are better compared with other models.

scholarly journals An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bader Alouffi ◽  
Abdullah Alharbi ◽  
Radhya Sahal ◽  
Hager Saleh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Short Term Memory ◽  
Learning Model ◽  
Dense Layer ◽  
Fake News ◽  
Learning Models ◽  
Proposed Model ◽  
Deep Learning Model ◽  
Machine Learning Models

Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID-19 outbreak. During the COVID-19 epidemic, fake news is widely spread. The best way to deal with this is early detection. Accordingly, in this work, we have proposed a hybrid deep learning model that uses convolutional neural network (CNN) and long short-term memory (LSTM) to detect COVID-19 fake news. The proposed model consists of some layers: an embedding layer, a convolutional layer, a pooling layer, an LSTM layer, a flatten layer, a dense layer, and an output layer. For experimental results, three COVID-19 fake news datasets are used to evaluate six machine learning models, two deep learning models, and our proposed model. The machine learning models are DT, KNN, LR, RF, SVM, and NB, while the deep learning models are CNN and LSTM. Also, four matrices are used to validate the results: accuracy, precision, recall, and F1-measure. The conducted experiments show that the proposed model outperforms the six machine learning models and the two deep learning models. Consequently, the proposed system is capable of detecting the fake news of COVID-19 significantly.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

scholarly journals Machine Learning-Based Malicious X.509 Certificates’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Traffic Analysis ◽  
Learning Models ◽  
Detection Model ◽  
Analysis Tools ◽  
Average Accuracy ◽  
Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

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