Peer-to-Peer Energy Trading Mechanism Based on Blockchain and Machine Learning for Sustainable Electrical Power Supply in Smart Grid

Jamil, Faisal; Iqbal, Naeem; Imran,; Ahmad, Shabir; Kim, Do‐Hyeun

doi:10.1109/access.2021.3060457

Cited by 150 publications

(59 citation statements)

References 67 publications

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“…Having the same platform, the authors in [126] demonstrated a P2P energy trading and energy sharing model having blockchain that reduces energy consumption at peak hours [127]. In a same way, Jamil et al [128] proposed an energy model based on blockchain having Hyperledger Fabric network between the prosumers and the consumers to aggregate the information for monitoring real-time load. They have also used the data analytics technique for extracting hidden patterns and information for right decision-making and managing energy distribution.…”

Section: Blockchain-based Energy Tradingmentioning

confidence: 99%

A Survey on Energy Trading in the Smart Grid: Taxonomy, Research Challenges and Solutions

et al. 2021

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The smart grid is generally studied as an efficient and powerful electric grid. With the assistance of information and communication technology (ICT), the electric grid can increase the performance of the power grid system with smart energy management. On the other hand, with the usage of renewable energy resources (RERs), smart energy storage, and new transmission technologies in the power grid system, various new features such as real-time monitoring, fast restoration, battery displays, automated outage management, etc. have been assimilated into the smart grid. These new features generate more complexity in energy transmission and constitute important challenges like low energy consumption, high energy cost, social welfare, etc. while designing energy trading mechanisms in the smart grid. In the Internet-of-Things (IoT) era, several scenarios such as micro-grids, energy harvesting networks, and vehicle-to-grid (V2G) networks are present where energy trading plays an important role. However, in these scenarios, there are energy transmission and distribution, security and privacy, energy consumption, system reliability, the criticality of data delivery, and a few more challenges caused by distrust, non-transparent, and uncertain energy markets. Motivated from these challenges, we present a four-layered architecture of energy trading used in the smart grid. We propose a comprehensive background regarding the main concepts of energy trading and the implication of enabling technologies that manage the energy imbalances in the smart grid. Then, we present a problem taxonomy based on incentive, mathematical, and simulation modeldriven approaches, which are widely used to control and maintain the energy trading mechanisms. Based on the findings from the literature, we also present a solution taxonomy with enabling technologies such as Energy Internet, Software-defined networking (SDN), and blockchain. In the end, a summary of future research directions based on the energy trading mechanisms is explored to provide deep insights to the readers.

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Section: Blockchain-based Energy Tradingmentioning

confidence: 99%

A Survey on Energy Trading in the Smart Grid: Taxonomy, Research Challenges and Solutions

et al. 2021

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“…Time-series is a sequence of random variables across time stamps upon which we apply tools and mathematical models to achieve the desired goal. Time-series analysis has been frequently reported in the literature for prediction with varying complexities and accuracies [52], [53]. Prediction of time-series involves predicting future data points based on historical data such that the error is minimized.…”

Section: Literature Reviewmentioning

confidence: 99%

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

et al. 2021

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Drilling data for groundwater extraction incur changes over time due to variations in hydrogeological and weather conditions. At any time, if there is a need to deploy a change in drilling operations, drilling companies keep monitoring the time-series drilling data to make sure it is not introducing any changes or new errors. Therefore, a solution is needed to predict groundwater levels (GWL) and detect a change in boreholes data to improve drilling efficiency. The proposed study presents an ensemble GWL prediction (E-GWLP) model using boosting and bagging models based on stacking techniques to predict GWL for enhancing hydraulic resource management and planning. The proposed research study consists of two modules; descriptive analysis of boreholes data and GWL prediction model using ensemble model based on stacking. First, descriptive analysis techniques, such as correlation analysis and difference mechanisms, are applied to investigate boreholes log data for extracting underlying characteristics, which is critical for enhancing hydraulic resource management. Second, an ensemble prediction model is developed based on multiple hydrological patterns using robust machine learning (ML) techniques to predict GWL for enhancing drilling efficiency and water resource management. The architecture of the proposed ensemble model involves three boosting algorithms as base models (level-0) and a bagging algorithm as a meta-model that combines the base models predictions (level-1). The base models consist of the following boosting algorithms; eXtreme Gradient Boosting (XGBoost), AdaBoost, Gradient Boosting (GB). The meta-model includes Random Forest (RF) as a bagging algorithm referred to as a level-1 model. Furthermore, different evaluation metrics are used, including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score. The performance of the proposed E-GWLP model is compared with existing ensemble and baseline models. The experimental results reveal that the proposed model performed accurately in respect of MAE, MSE, and RMSE of 0.340, 0.564, and 0.751, respectively. The MAPE and R2 score of our proposed approach is 12.658 and 0.976, respectively, which signifies the importance of our work. Moreover, experimental results suggest that E-GWLP model is suitable for sustainable water resource management and improves reservoir engineering.

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“…In [39], the blockchain technology explained in detail for the automatic outline selection of charging stations of vehicles. In [40,41], the power supply chain based on a smart grid for the sustainable electrical power and indoor navigation is presented. This process involves a combination of machine learning and blockchain network for a peer-to-peer energy trading approach.…”

Section: Taxi Demand Based On Blockchain Approachmentioning

confidence: 99%

A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach

Shahbazi

Byun

2021

Sensors

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The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers. The majority of research has focused on forecasting the pick-up part of demand service and specifying the interconnection of spatial and temporal correlations. In this study, the main focus is to overcome the access point of non-registered users for having fake transactions using taxi services and predicting taxi demand pick-up and drop-off information. The integration of machine learning techniques and blockchain framework is considered a possible solution for this problem. The blockchain technique was selected as an effective technique for protecting and controlling the real-time system. Historical data analysis was processed by extracting the three higher related sections for the intervening time, namely closeness and trend. Next, the pick-up and drop-off taxi prediction task was processed based on constructing the components of multi-task learning and spatiotemporal feature extraction. The combination of feature embedding performance and Long Short-Term Memory (LSTM) obtain the pick-up and drop-off correlation by fusing the historical data spatiotemporal features. Finally, the taxi demand pick-up and drop-off prediction were processed based on the combination of the external factors. The experimental result is based on a real dataset in Jeju Island, South Korea, to show the proposed system’s efficacy and performance compared with other state-of-art models.

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Peer-to-Peer Energy Trading Mechanism Based on Blockchain and Machine Learning for Sustainable Electrical Power Supply in Smart Grid

Cited by 150 publications

References 67 publications

A Survey on Energy Trading in the Smart Grid: Taxonomy, Research Challenges and Solutions

A Survey on Energy Trading in the Smart Grid: Taxonomy, Research Challenges and Solutions

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach

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