International Journal of Smart Sensor and Adhoc Network


Renewable energy sources are becoming more popular, providing a much-needed alternative to traditional, limited, and climate-unfriendly energy sources. Wireless sensors, cloud computing, cyber security, and wide-area monitoring are basic communication and control technologies for smart grid applications. Design of communication and control architectures for the adoption of smart energy grids for rural loads and distributed energy, including energy storage solutions. In this work, a Machine Learning module called scikit-learn is used for pre-processing of labeled input data by using StandardScaler, KFold for cross-validation, and Confusion matrix for measuring performance. Also, the ML technique uses the binary classification method to divide the ‘stabf’ data into two parts as stable and unstable. Here deep learning-based Artificial Neural Network (ANN) has been used to evaluate the result and to predict new grid data to enhance stability. ANN takes 12 input nodes in the input layer and three hidden layers out of which two hidden layer takes 24 nodes and another one takes 12 nodes and an output layer consisting of a single node. Adam optimizer has been used for model compilation and loss function calculation ‘binary_crossentropy’ is used. Finally, after successful completion of the evaluation process, this model gives a test accuracy result of 98.33%.





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