HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

Akhtaruzzaman, Md.; Hasan, Mohammad Kamrul; Kabir, S. Rayhan; Abdullah, Siti Norul Huda Sheikh; Sadeq, Muhammad Jafar; Hossain, Eklas

doi:10.1109/access.2020.3040083

Cited by 66 publications

(29 citation statements)

References 147 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e QoE considers the IoV experience to ensure high quality of data transmission by continuously measuring the QoE of the network and updating. For IoV end users and due to the rapid change of IoV communication topology, the user's quality of experience is considered as one of the main challenges in IoV networks [28]. e flexible and scalable connection between integrated components of the IoV system i.e., vehicles, sensors, actuators, humans, and machines, is vital for IoV, which must fit with the requirement of user perception enhancement to decrease the power consumption.…”

Section: Artificial Intelligence-enabled Quality Of Experience Optimimentioning

confidence: 99%

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Ali

Hasan

Hassan

et al. 2021

Security and Communication Networks

Self Cite

123

View full text Add to dashboard Cite

Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.

show abstract

Section: Artificial Intelligence-enabled Quality Of Experience Optimimentioning

confidence: 99%

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Ali

Hasan

Hassan

et al. 2021

Security and Communication Networks

Self Cite

123

View full text Add to dashboard Cite

show abstract

“…A unique characteristic feature of standard ELM algorithm is that they have only one large hidden layer which remains untrained. It has some advantages like training the network more quickly and reduced overfitting but comes along with some feature learning constraints [28]. The authors in [29] put forward a novel method named recurrent extreme learning machines which incorporates ELM with RNN.…”

Section: Related Workmentioning

confidence: 99%

Multi-Horizon Electricity Load and Price Forecasting Using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework

Azam

Younis

2021

IEEE Access

View full text Add to dashboard Cite

Accurate system marginal price and load forecasts play a pivotal role in economic power dispatch, system reliability and planning. Price forecasting helps electricity buyers and sellers in an energy market to make effective decisions when preparing their bids and making bilateral contracts. Despite considerable research work in this domain, load and price forecasting still remains to be a complicated task. Various uncertain elements contribute to electricity price and demand volatility, such as changes in weather conditions, outages of large power plants, fuel cost, complex bidding strategies and transmission congestion in the power system. Thus, to deal with such difficulties, we propose a novel hybrid deep learning method based upon bidirectional long short-term memory (BiLSTM) and a multi-head self-attention mechanisms that can accurately forecast locational marginal price (LMP) and system load on a day-ahead basis. Additionally, ensemble empirical mode decomposition (EEMD), a data-driven algorithm, is used for the extraction of hidden features from the load and price time series. Besides that, an intuitive understanding of how the proposed model works under the hood is demonstrated using different interpretability use cases. The performance of the presented method is compared with existing well-known techniques applied for short-term electricity load and price forecast in a comprehensive manner. The proposed method produces considerably accurate results in comparison to existing benchmarks.INDEX TERMS Deep learning, energy markets, ensemble empirical mode decomposition, multi-head self-attention, short-term load and price forecasting, kalman smoothing, model interpretability.

show abstract

“…The significant advancement has been accomplished for the communication infrastructure with the special technologies that include artificial intelligence, Internet of Things (IoT), 4G, and 5G data access [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. This advancement enriched the communication infrastructure for smart grid protection, measurement, and control system in a sophisticated manner.…”

Section: Recent Issues and Challenges Of Communication Systems In Smart Gridmentioning

confidence: 99%

“…The issues and the challenges of SCADA and PMU based WAM system for substation automation are discussed below. 3 Wireless Communications and Mobile Computing associations to access substation devices, reduce maintenance cost, and support the execution of additional substation mechanization applications [25][26][27][28][29]. The coordination of sequential protocol substation devices onto an Ethernet structure is a significant test.…”

Section: Recent Issues and Challenges Of Communication Systems In Smart Gridmentioning

confidence: 99%

Communication Delay Modeling for Wide Area Measurement System in Smart Grid Internet of Things Networks

Hasan

Islam

Shafiq

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

We present communication frameworks, models, and protocols of smart grid Internet of Things (IoT) networks based on the IEEE and IEC standards. The measurement, control, and monitoring of grid being achieved through phasor measurement unit (PMU) based wide area measurement (WAM) framework. The WAM framework applied the IEEE standard C37.118 phasor exchange protocol to collect grid data from various substation devices. The existing frameworks include the IEC 61850 protocol and programmable logic controllers (PLCs) based supervisory control and data acquisition (SCADA) system. These protocols have been selected as per the smart grid configuration and communication design. However, the existing frameworks have severe synchronization errors due to the communication delays of IoT networks in the smart grid. Therefore, this article designs the timing mechanism and a delay model to reduce the timing delay and boost real-time measurement, monitoring, and control performance of the smart grid WAM applications. The result shows that the proposed model outperformed the existing WAM system.

show abstract

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

Cited by 66 publications

References 147 publications

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Multi-Horizon Electricity Load and Price Forecasting Using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework

Communication Delay Modeling for Wide Area Measurement System in Smart Grid Internet of Things Networks

Contact Info

Product

Resources

About