Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and utilities can benefit from a predictive study of electricity demand and pricing. In this study, we used a new machine learning approach called AdaBoost to identify key features from an ISO-NE dataset that includes daily consumption data over eight years. Moreover, the DT classifier and RF are widely used to extract the best features from the dataset. Moreover, we predicted the electricity load and price using machine learning techniques including support vector machine (SVM) and deep learning techniques such as a convolutional neural network (CNN). Coronavirus herd immunity optimization (CHIO), a novel optimization approach, was used to modify the hyperparameters to increase efficiency, and it used classifiers to improve the performance of our classifier. By adding additional layers to the CNN and fine-tuning its parameters, the probability of overfitting the classifier was reduced. For method validation, we compared our proposed models with several benchmarks. MAE, MAPE, MSE, RMSE, the f1 score, recall, precision, and accuracy were the measures used for performance evaluation. Moreover, seven different forms of statistical analysis were given to show why our proposed approaches are preferable. The proposed CNN-CHIO and SVM techniques had the lowest MAPE error rates of 6% and 8%, respectively, and the highest accuracy rates of 95% and 92%, respectively.

Traffic delays are not wholly new and are a well-known problem that impacts many of the world’s populations through disruptions and pollution. The rising urbanization and quantity of powered road vehicles necessitate a greater traffic control demand to maintain flow and avoid jams. In order to understand the notion of sustainable transportation, this study first examined sustainable transportation systems. This research then assessed Pakistan’s present transportation infrastructure and urban transportation to find the most reasonable and sustainable alternative to reduce congestion. The Taxila intersection was utilized as a pilot study area because of its vicinity to Pakistan’s leading economic hubs (i.e., industrial estates and the twin cities of Islamabad and Rawalpindi). The study used multi-criteria decision making (MCDM) techniques, including the fuzzy AHP, TOPSIS, VIKOR, and traffic simulation software, to determine the optimal solution for a more sustainable transportation system, and reducing traffic congestion. A pairwise comparison of the criteria and alternatives was made using a survey. This survey was used to look into the perspectives of various stakeholders and experts. The outcomes of the fuzzy AHP-TOPSIS and fuzzy AHP-VIKOR revealed that a flyover is the best alternative. In contrast, the best alternative, according to the software was a parking area. Ultimately, we assessed our results using the literature, and site observation, and concluded that a parking area would be the most sustainable alternative in the Taxila intersection.