Modeling and Simulation of Power consumption in smart grid tow
Implementation plan:
——————————
Step1: Initially we construct a smart grid, perform data collection and the preprocessing is done to remove missing values, noise and redundancy using Z-Score Normalization.
Step2: Next we perform the Feature Extraction process using Spatial Temporal Correlation (STC) to establish the dynamic nature of the power consumption pattern.
Step3: Then the extracted data will be transmitted securely by undergoing Distributed Authentication and Authorization (DAA) protocol to Ensure data integrity, privacy and then stored on a blockchain on cloud.
Step4: Then we perform Demand based prediction using “Long-Short-Term-Memory based Recurrent Neural Network with Improved Sparrow Search Algorithm” (LSTM-RNN-ISSA) for accurate load forecasting.
Step5: Next Smart grid communication is performed using Blockchain-Based Smart Energy Trading with Adaptive Volt-VAR Optimization (BSET-AVVO) to create a reliable, low-latency communication network.
Step6: Finally, we verify the process using various performance metrics such as,
6.1: Number of samples vs Mean Squared Error (MSE)
6.2: Time (h) vs Power Consumption (KW)
6.3: Number of samples vs average latency (ms)
6.4: Time (s) vs throughput
6.5: Number of samples vs response time (ms)
[The process based on your requirement]
Software Requirement:
——————————–
1. Development Tool: Python – 3.11.4 or Above version
2. Operating System: Windows 10 (64-bit)
We perform the EXISTING process based on the Reference 1: Title: AI-enabled metaheuristic optimization for predictive management of renewable energy production in smart grids
