The advances in smart grids are enabling huge amount of data to be aggregated and analyzed for various smart grid applications. However, the traditional smart grid data management systems cannot scale and provide sufficient storage and processing capabilities. To address these challenges, this paper presents a smart grid big data eco-system based on the state-of-the-art Lambda architecture that is capable of performing parallel batch and real-time operations on distributed data. Further, the presented eco-system utilizes a Hadoop Big Data Lake to store various types of smart grid data including smart meter, images and video data. An implementation of the smart grid big data eco-system on a cloud computing platform is presented. To test the capability of the presented eco-system, real-time visualization and data mining applications were performed on real smart grid data. The results of those applications on top of the eco-system suggest that it is capable of performing numerous smart grid big data analytics.

Extracting electric vehicle charging loads is an important aspect that enables smart grid operators to make informed and intelligent decisions about conserving power and promoting the reliability of the electrical grid. This paper presents an unsupervised algorithm to extract the electric vehicle charging loads non-intrusively from the smart meter data. The proposed algorithm can run on low-frequency smart meter sampling data and only requires the real power measurement, which is the type of data communicated and recorded by most smart meters. Validation results on real aggregated household loads have shown that the proposed approach is a promising solution to extract electric vehicle charging loads and that the approach can effectively mitigate the interference of other appliances that have similar load behaviors as electric vehicles. Further, the extraction of such load behaviors can be aggregated and open further smart grid analyses and studies.

Intrusion detection systems (IDS) are essential for the protection of advanced communication networks. These systems were primarily designed to identify particular patterns, signatures, and rule violations. Machine Learning and Deep Learning approaches have been used in recent years in the field...

Huge amounts of educational data are being produced, and a common challenge that many educational organizations confront, is finding an effective method to harness and analyze this data for continuously delivering enhanced education. Nowadays, the educational data is evolving and has become large in volume, wide in variety and high in velocity. This produced data needs to be handled in an efficient manner to extract value and make informed decisions. For that, this paper confronts such data as a big data challenge and presents a comprehensive platform tailored to perform educational big data analytical applications. Further, present an effective environment for non-data scientists and people in the educational sector to apply their demanding educational big data applications. The implementation stages of the educational big data platform on a cloud computing platform and the organization of educational data in a data lake architecture are highlighted. Furthermore, two analytical applications are performed to test the feasibility of the presented platform in discovering knowledge that potentially promotes the educational institutions.