Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions impact all RES, causing changes in the amount of electricity produced by these sources. Furthermore, availability is dependent on daily or annual cycles. Although smart meters allow real-time demand prediction, precise models that predict the electricity produced by RES are also required. Prediction Models (PMs) accurately guarantee grid stability, efficient scheduling, and energy management. For example, the SG must be smoothly transformed into the conventional energy source for that time and guarantee that the electricity generated meets the predicted demand if the model predicts a period of Renewable Energy (RE) loss. The literature also suggests scheduling methods for demand-supply matching and different learning-based PMs for sources of RE using open data sources. This paper developed a model that accurately replicates a microgrid, predicts demand and supply, seamlessly schedules power delivery to meet demand, and gives actionable insights into the SG system’s operation. Furthermore, this work develops the Demand Response Program (DRP) using improved incentive-based payment as cost suggestion packages. The test results are valued in different cases for optimizing operating costs through the multi-objective ant colony optimization algorithm (MOACO) with and without the input of the DRP.

Existing methods use static path identifiers, making it easy for attackers to conduct DDoS flooding attacks. Create a system using Dynamic Secure aware Routing by Machine Learning (DAR-ML) to solve healthcare data. A DoS detection system by ML algorithm is proposed in this paper. First, to access the user to see the authorized process. Next, after the user registration, users can compare path information through correlation factors between nodes. Then, choose the device that will automatically activate and decrypt the data key. The DAR-ML is traced back to all healthcare data in the end module. In the next module, the users and admin can describe the results. These are the outcomes of using the network to make it easy. Through a time interval of 21.19% of data traffic, the findings demonstrate an attack detection accuracy of over 98.19%, with high precision and a probability of false alarm.