Amongst energy-related CO${}_2$ emissions, electricity is the largest single contributor, and with the proliferation of electric vehicles and other developments, energy use is expected to increase. Load forecasting is essential for combating these issues as it balances demand and production and contributes to energy management. Current state-of-the-art solutions such as recurrent neural networks (RNNs) and sequence-to-sequence algorithms (Seq2Seq) are highly accurate, but most studies examine them on a single data stream. On the other hand, in natural language processing (NLP), transformer architecture has become the dominant technique, outperforming RNN and Seq2Seq algorithms while also allowing parallelization. Consequently, this paper proposes a transformer-based architecture for load forecasting by modifying the NLP transformer workflow, adding N-space transformation, and designing a novel technique for handling contextual features. Moreover, in contrast to most load forecasting studies, we evaluate the proposed solution on different data streams under various forecasting horizons and input window lengths in order to ensure result reproducibility. Results show that the proposed approach successfully handles time series with contextual data and outperforms the state-of-the-art Seq2Seq models.

Smart building digital twins aim to virtually replicate the static and dynamic building characteristics through real-time connectivity between the virtual and physical counterparts. The virtual replica of the building can then be leveraged to monitor the current state, predict the future state, and take proactive measures to ensure optimal operation. Despite its potential, smart building digital twin research is at an early stage compared to manufacturing and aerospace fields. One of the major impediments to adopting digital twin technology in smart buildings is the lack of interoperability, primarily between Building Information Modeling (BIM) and Internet of Things (IoT) data sources. Consequently, this paper presents a novel multi-layer digital twin architecture for smart buildings called BIM-IoT Data Integration (BIM-IoTDI) to enable semantic interoperability among smart building digital twin applications. A detailed framework is presented based on the newly developed architecture, introducing an ontology-based query mediation method that provides integrated data access. The feasibility of the BIM-IoTDI architecture and framework is demonstrated using a case study building and real-time monitoring application. Furthermore, the performance of the new query mediation method is evaluated and compared to an existing BIM-IoT data integration approach. According to the evaluation results, the BIM-IoTDI architecture and framework are better suited to supporting the envisioned smart building digital twin capabilities.

The ability to detect and process anomalies for Big Data in real-time is a difficult task. The volume and velocity of the data within many systems makes it difficult for typical algorithms to scale and retain their real-time characteristics. The pervasiveness of data combined with the problem that many existing algorithms only consider the content of the data source; e.g. a sensor reading itself without concern for its context, leaves room for potential improvement. The proposed work defines a contextual anomaly detection framework. It is composed of two distinct steps: content detection and context detection. The content detector is used to determine anomalies in real-time, while possibly, and likely, identifying false positives. The context detector is used to prune the output of the content detector, identifying those anomalies which are considered both content and contextually anomalous. The context detector utilizes the concept of profiles, which are groups of similarly grouped data points generated by a multivariate clustering algorithm. The research has been evaluated against two real-world sensor datasets provided by a local company in Brampton, Canada. Additionally, the framework has been evaluated against the open-source Dodgers dataset, available at the UCI machine learning repository, and against the R statistical toolbox.

The Resource Description Framework (RDF) is a flexible model for representing information about resources on the Web. As a W3C (World Wide Web Consortium) Recommendation, RDF has rapidly gained popularity. With the widespread acceptance of RDF on the Web and in the enterprise, a huge amount of RDF data is being proliferated and becoming available. Efficient and scalable management of RDF data is therefore of increasing importance. RDF data management has attracted attention in the database and Semantic Web communities. Much work has been devoted to proposing different solutions to store RDF data efficiently. This paper focusses on using relational databases and NoSQL (for ‘not only SQL (Structured Query Language)’) databases to store massive RDF data. A full up-to-date overview of the current state of the art in RDF data storage is provided in the paper.

The emergence of Big Data has had profound impacts on how data are stored and processed. As technologies created to process continuous streams of data with low latency, Complex Event Processing (CEP) and Stream Processing (SP) have often been related to the Big Data velocity dimension and used in this context. Many modern CEP and SP systems leverage cloud environments to provide the low latency and scalability required by Big Data applications, yet validating these systems at the required scale is a research problem per se. Cloud computing simulators have been used as a tool to facilitate reproducible and repeatable experiments in clouds. Nevertheless, existing simulators are mostly based on simple application and simulation models that are not appropriate for CEP or for SP. This article presents CEPSim, a simulator for CEP and SP systems in cloud environments. CEPSim proposes a query model based on Directed Acyclic Graphs (DAGs) and introduces a simulation algorithm based on a novel abstraction called event sets. CEPSim is highly customizable and can be used to analyse the performance and scalability of user-defined queries and to evaluate the effects of various query processing strategies. Experimental results show that CEPSim can simulate existing systems in large Big Data scenarios with accuracy and precision. CEPSim, a simulator for cloud-based Complex Event Processing systems, is proposed.CEPSim query model is based on Directed Acyclic Graphs.CEPSim simulation algorithm is based on a novel abstraction called event sets.Custom operator placement and scheduling algorithms can be used in simulations.Experimental results showed that CEPSim is effective for Big Data simulations.