Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management

Li, Joey H.; Herdem, Münür Sacit; Nathwani, Jatin; Wen, John Z.

doi:10.1016/j.egyai.2022.100208

Cited by 119 publications

(51 citation statements)

References 46 publications

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“…To ensure reliable performance and efficient use of energy resources, AI models predict energy consumption, load profiles, and resource status. Li et al 113 provides a comprehensive view of the application of AI, blockchain, Internet of Things (IoT) devices, and big data technologies in smart energy management. It states that the efficient integration of these technologies will be an important factor in transitioning the energy sector to a low‐carbon system.…”

Section: Application Of Artificial Intelligence In Blockchain‐based E...mentioning

confidence: 99%

Towards the net zero carbon future: A review of blockchain‐enabled peer‐to‐peer carbon trading

Parhamfar,

Sadeghkhani,

Adeli

2024

Energy Science & Engineering

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The increasing trend of energy generation and management systems towards decentralized structures such as using renewable energy resources makes it necessary to use digital and smart platforms for exchanging information and even conducting financial transactions in a decentralized manner, known as the peer‐to‐peer model. The decentralized transaction verification of cryptocurrencies makes it possible to use these encrypted currencies and decentralized blockchain networks in energy management systems and carry out financial transactions related to carbon trading. Carbon and other greenhouse gas (GHG) emission trading systems reduce the competitiveness of fossil fuel projects in the market and accelerate investment in low‐carbon energy sources such as wind and photovoltaic power generation units. This market mechanism allows large entities such as countries and companies that emit GHGs into the atmosphere to buy and sell these gases. This paper reviews the blockchain solutions developed for carbon markets. Studies related to the design of smart contracts in the platform of blockchain are investigated. Special cryptocurrencies that are used in the field of green energy transactions and carbon trading are introduced. In addition, the application of artificial intelligence and game theory in energy trading is stated. The study of different blockchain frameworks for carbon trading shows that the use of decentralized platforms in carbon trading can have a significant impact on the trend towards low‐carbon measures and achieving the goals of the Kyoto Treaty, increasing the value of green cryptocurrencies and the volume of transactions. These technologies offer a promising avenue for creating a more decentralized, efficient, and environmentally conscious energy ecosystem.

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Section: Application Of Artificial Intelligence In Blockchain‐based E...mentioning

confidence: 99%

Towards the net zero carbon future: A review of blockchain‐enabled peer‐to‐peer carbon trading

Parhamfar,

Sadeghkhani,

Adeli

2024

Energy Science & Engineering

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“…Other related works include Afzal et al (2020), Alsunaidi and Khan (2022), Bhalaji and Vinayak (2020), Dhillon et al (2017), Divya et al (2022), Fakhri and Mutijarsa (2018), Huh et al (2017), Khalid et al (2020), Li et al (2023), Mohanta et al (2020), Miglani et al (2020), Pethuru Raj and Evangeline (2020), Rashid et al (2019) and Xiao (2018).…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of a blockchain enabled domestic power consumption monitoring and forecasting system

Fowdur,

Sanghan

2024

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Purpose The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting. Design/methodology/approach The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms. Findings The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively. Originality/value A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

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“…These advancements aim to improve accuracy and efficiency in processing large datasets, particularly in sensitive domains like healthcare and finance. Edge computing allows for faster decision-making, reduced latency, and more efficient handling of streaming data, crucial for applications like IoT devices [1,2].…”

Section: Privacy and Data Security Issuesmentioning

confidence: 99%

Optimization of Big Data Processing and Analysis Processes in the Field of Data Analytics Through the Integration of Data Engineering and Artificial Intelligence

Nesterov

2024

cit

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In this article, we provide an overview of the current state of artificial intelligence (AI) and big data analytics, which are the most needed needs of our age. This report is designed to provide an overview of the latest research and achievements in this rapidly growing field and to highlight, check and complement the many ways in which AI can be combined to improve data. The most important developments in the field of data analysis demonstrate the development of machine learning models, the importance of explaining AI, and the use of AI in computing. Methodology: for this research we have analyse the latest trend in the related field of AI and data engineering. We have short listed nine main papers for this report. Meta-analysis requires the use of descriptive and/or statistical methods to collect data from multiple studies on a particular topic. These ideas help build knowledge from many studies in both qualitative and quantitative ways. Results: The article provides an overview of the complexities of integrating large amounts of data with intelligence, exploring topics such as data integrity, social and ethical issues. Additionally, it introduces new applications across multiple industries and reveals opportunities to transform environmental monitoring, supply chain operations, cybersecurity, and health monitoring. Overall, this article provides the reader with an in-depth understanding of the latest advances and challenges in data mining and intelligence-based analysis. Conclusion: It contributes to the ongoing debate on how AI can be used to gain meaningful insights from massive data sets, and provides a forward-looking perspective on the potential impacts on multiple sectors of the economy and society in general. Another new concept that expresses the openness of AI algorithms is descriptive intelligence (XAI). Understanding and interpreting intelligence decisions is important because it plays an important role in information processing, especially in areas that require compliance with ethical and legal standards.

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Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management

Cited by 119 publications

References 46 publications

Towards the net zero carbon future: A review of blockchain‐enabled peer‐to‐peer carbon trading

Towards the net zero carbon future: A review of blockchain‐enabled peer‐to‐peer carbon trading

Performance analysis of a blockchain enabled domestic power consumption monitoring and forecasting system

Optimization of Big Data Processing and Analysis Processes in the Field of Data Analytics Through the Integration of Data Engineering and Artificial Intelligence

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