AI based Smart Grid Research Topics

This technology is used to predict the power consumption from a smart grid using AI and Blockchain technology. There are many significance of this technique compared with the traditional one. To learn more about this technology, read further to this paper.

  1. Define AI-based prediction of power consumption in smart grid on smart city using blockchain technology

Algorithm of artificial intelligence is used to predict the power consumption of a smart grid in the smart city by analyzing the user behaviour, historic data, weather patterns and some other variables for predicting electricity usage. This technique helps to manage resources efficiently, manage demands and effective energy distribution. By adding blockchain technology to this, there will be a significant increase in transparency and security by ensuring data exchange between the nodes of the grid network, maintaining records of energy distribution and consumption also secure transactions. By this way the trustworthiness and reliability of the system is increased.

  1. What is AI-based prediction of power consumption in smart grid on smart city using blockchain technology?

The blockchain technology along with AI algorithm, predicts the energy demand also provide better distribution of it by maintaining transparency and security. It analyses the usage patterns, secure and transparent transactions, optimizing resources also the data integrity of grid to manage energy. Blockchain increases trust by maintaining a tamper-proof record for energy distribution and consumption while AI maintains efficiency to provide an efficient and reliable energy grid for smart city.

  1. Where smart grid is used?

This technology behaves as modern electrical network with using digital communication to increase reliability and efficiency of the network in rural and urban areas. It is also used in areas of industries, cities, remote regions and suburbs to enhance energy distribution, to control energy demand, combine renewable sources of energy and to create two way communications within consumers and suppliers. Smart grid is being the important factor in electric vehicles, wind and solar energy systems also in maintaining the peak loads. This smart grid serves as the most efficient energy infrastructure in many areas.

  1. Why prediction of power consumption in smart grid is proposed? Previous technology issues

The main challenge of this technology arises when using data of large scale. Here comes the computational complexity and problem in data transmission which could not be in a distributed manner and the energy consumption predictions which is not applicable for long-term.

Computational complexity due to large scale data: When there is a need of taking a precise data as input from a large scale of data, there arises the computational complexity.

Centralized formulation: When one centralized node is having all the power, then the system struggles to divide the task with other nodes of the network, so they become less flexible, scalable and resilient.

Short term focus as primary limitation: The forecasting can be done only on short term, long term prediction is not possible. This is the major limitation of its scalability and application.

Limited communication: This is useful for the end user communication. The limitations of it are it cannot communicate computing resources, delay, resource consumption and also affects system overhead of a path in communication.

Transmission path and standards: In terms of latency affects, assumptions on bandwidth and technological dynamics this can affect the bandwidth assumptions and transmission path.

  1. Algorithms / Protocols

The algorithms provided for Smart grid to overcome the previous issues faced by it are mentioned here: “Spatial Temporal Correlation” (STC), “Long-Short-Term-Memory based Recurrent Neural Network with Improved Sparrow Search Algorithm” (LSTM-RNN-ISSA), “Task-Oriented Communication mechanism”, “Distributed Authentication and Authorization” (DAA) protocol, “Blockchain-Based Smart Energy Trading with Adaptive Volt-VAR Optimization” (BSET-AVVO) algorithm and “Z-Score normalization”.

  1. Simulation results / Parameters

The approaches which were proposed to overcome the issues faced by Smart grid technology are tested using different methodologies to analyze its performance. The comparison is done by using metrics like Time (h) vs. Power Consumption (KW), Time (s) vs. throughput, Number of samples vs. Mean Squared Error (MSE), Number of samples vs. response time (ms) and Number of samples vs. average latency (ms).

  1. Dataset LINKS / Important URL

Here are some of the links provided for you below to gain more knowledge about Smart grid technology which can be useful for you:

  1. Smart grid Applications

The applications of Smart grid have increased in many areas and business such as Demand Response Systems, Electric Vehicle (EV) Charging Infrastructure, Energy Storage Integration, Advanced Metering Infrastructure (AMI), Distributed Energy Resources (DERs), Cyber Security Measures, Predictive Analytics and AI and Grid Automation.

  1. Topology

The topologies used in smart grid for smart city with AI based prediction along with blockchain technology are mentioned here: Feature Extraction, Demand based prediction, Data Collection and pre-processing, smart grid communication strategy also Transmission and storage using blockchain.

  1. Environment

This system is being more environmental friendly by accurately predicting the energy needed by AI algorithms in terms of optimized resource allocation which will end up in efficient use of energy and reduce of waste. By combining AI with Blockchain it ensures transparent and secure transactions, data management also increasing reliability and trust on sources of renewable energy. This system contributes to more sustainable environment with reduce in carbon emission, making use of clean energy and maintaining an eco-friendly infrastructure in smart cities.

  1. Simulation Tools

Here we provide some simulation software for previous works, which is established with the usage of NS 3 tool with 3.36 or above version.

  1. Results

After going through this research to find the power consumption in smart grid by using AI prediction and blockchain technology, you can understand in detail about this technology, applications of this technology, and different topologies of it, algorithms followed by it and also about the limitations of it.

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