Stock price prediction anticipates future stock prices using historical data and computational models to assist and guide investing decisions. In financial forecasting, accuracy and efficacy in stock price prediction are essential for making better choices. This research describes a hybrid deep learning strategy for improving the extraction and interpretation of the crucial details from stock price time series data. Traditional approaches confront challenges such as computational complexity and nonlinear stock prices. The suggested method pre-processes stock data with Moving Average Z-Transformation, which emphasises long-term trends and reduces fluctuations in the short term. It combines a Transformed Moving Average Fast-RNN Hybrid with Advanced CNNs to create an efficient computational framework. The Enhanced Deep-CNN layer comprises convolutional layers, batch normalisation, leaky ReLU activations, dropout, max pooling and a dense layer. The performance of the model is quantified using metrics including Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-squared (R2). It shows superior prediction accuracy with MAEs of 0.28, 0.15, 0.34, 0.17, and 0.13 for Kotak, ICICI, Axis, and SBI, respectively, outperforming previous models. These measurements provide detailed information about the model's predictive skills, proving its ability to improve stock price forecast accuracy significantly.

As the global demand for electricity is growing continuously, the sources use more fossil fuels to generate electricity which in turn increases the level of carbon dioxide in the atmosphere. Moreover the electrical system becomes unreliable during the peak hours if the demand for electricity is very high. So there is a need to have a grid system which can handle these cases in a smarter way. A Smart Grid is such an electrical grid system which can control and manage electricity demand in a more reliable and economic manner using various energy efficient resources and a variety of operational measures like smart meters, smart appliances and smart communication system. The smart grid uses a technique called energy demand management at consumer side which motivates the consumers to control and reduce their demand for energy during peak hours. This makes the whole system more reliable and efficient. The demand side management (DSM) includes various methods such as increasing awareness among the consumers and giving them some financial incentives which can encourage them to be a part of the DSM program. In this paper a novel Demand Side Management technique has been proposed for a typical smart grid scenario which comprises users with energy storage devices using a metaheuristic approach to have an optimal load scheduling that results in reduced peak hour demands.