Today’s energy resources are closer to consumers thanks to sustainable energy and advanced metering infrastructure (AMI), such as smart meters. Smart meters are controlled and manipulated through various interfaces in smart grids, such as cyber, physical and social interfaces. Recently, a large number of non-technical losses (NTLs) have been reported in smart grids worldwide. These are partially caused by false data injections (FDIs). Therefore, ensuring a secure communication medium and protected AMIs is critical to ensuring reliable power supply to consumers. In this paper, we propose a novel Big Data-driven solution that employs machine learning, deep learning and parallel computing techniques. We additionally obtained robust statistical features to detect the FDIs based cyber threats at the distribution level. The performance of the proposed model for NTL detection is investigated using private smart grid datasets in the Turkish distribution network for AMI-level cyber threats, and the results are compared to state-of-the-art machine learning algorithms used for NTL classification problems. Our approach shows promising results, as the accuracy, specificity, and precision metrics of most classifiers are above 90% and false positive rates vary between 0.005 to 0.027.

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

This study highlights the intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance in the context of plant disease identification. It emphasizes the importance of rigorous dataset evaluation and selection protocols to ensure reliable and generalizable classification outcomes. Through a comprehensive examination of publicly available plant disease datasets, focusing on their performance as measured by GLCM metrics, this research identified dataset_2 (D2), a database of leaf images, as the top performer across all GLCM analyses. These datasets were then utilized to train the DarkNet19 deep learning model, with D2 exhibiting superior performance in both GLCM analysis and DarkNet19 training (achieving about 91% testing accuracy) according to performance metrics such as accuracy, precision, recall, and F1-score. The datasets other than dataset_1 and 2 exhibited significantly low classification performance, particularly in supporting GLCM analysis. The findings underscore the need for transparency and rigor in dataset selection, particularly given the abundance of similar datasets in the literature and the growing trend of utilizing deep learning methods in future scientific research.