Multisource energy data, including from distributed energy resources and its multivariate nature, necessitate the integration of robust data predictive frameworks to minimise prediction error. This work presents a hybrid deep learning framework to accurately predict the energy consumption of different building types, both commercial and domestic, spanning different countries, including Canada and the UK. Specifically, we propose architectures comprising convolutional neural network (CNN), an autoencoder (AE) with bidirectional long short-term memory (LSTM), and bidirectional LSTM BLSTM). The CNN layer extracts important features from the dataset and the AE-BLSTM and LSTM layers are used for prediction. We use the individual household electric power consumption dataset from the University of California, Irvine to compare the skillfulness of the proposed framework to the state-of-the-art frameworks. Results show performance improvement in computation time of 56% and 75.2%, and mean squared error (MSE) of 80% and 98.7% in comparison with a CNN BLSTM-based framework (EECP-CBL) and vanilla LSTM, respectively. In addition, we use various datasets from Canada and the UK to further validate the generalisation ability of the proposed framework to underfitting and overfitting, which was tested on real consumers’ smart boxes. The results show that the framework generalises well to varying data and constraints, giving an average MSE of ∼0.09 across all datasets, demonstrating its robustness to different building types, locations, weather, and load distributions.

The Industrial Internet of Things (IIoT) facilitates enhanced automation, predictive maintenance, real-time monitoring, and data analytics across various sectors, including manufacturing, energy, transportation, agriculture, and supply chain management, thereby improving productivity, efficiency, and operational safety. However, as IIoT networks continue to expand, it is imperative to secure them against increasingly sophisticated cyber threats. Deep Learning (DL) techniques have been extensively utilized for intrusion detection within IIoT systems. Nevertheless, addressing the class imbalance problem remains a significant challenge. The underrepresentation of certain attack types in training data frequently results in the development of DL models that struggle to accurately detect these categories of malicious activities. This limitation represents considerable risks to the security of IIoT networks, as undetected attacks and false alarms may lead to severe operational disruptions. In this paper, we propose a multi-stage deep learning (MSDL) method specifically designed to enhance intrusion detection within IIoT networks by addressing the class imbalance issue. We assessed the effectiveness of our approach utilizing two highly imbalanced datasets: X-IIoTID and WUSTL-IIoT. Our experimental findings indicate that the proposed MSDL method surpasses the baseline DL models as well as state-of-the-art oversampling and undersampling techniques. Specifically, the MSDL method exhibits significant improvements in recognizing minority-class attacks that are frequently misclassified. Consequently, the implementation of the MSDL for intrusion detection is anticipated to strengthen the overall security and resilience of IIoT systems, providing stronger protection against a diverse array of cyber threats in industrial applications.

Reliable communication is the backbone of advanced metering infrastructure (AMI). Within the AMI, the neighbourhood area network (NAN) transports a multitude of traffic, each with unique requirements. In order to deliver an acceptable level of reliability and latency, the underlying network, such as the wireless mesh network(WMN), must provide or guarantee the quality-of-service (QoS) level required by the respective application traffic. Existing WMN routing protocols, such as optimised link state routing (OLSR), typically utilise a single metric and do not consider the requirements of individual traffic; hence, packets are delivered on a best-effort basis. This paper presents a QoS-aware WMN routing technique that employs multiple metrics in OLSR optimal path selection for AMI applications. The problems arising from this approach are non deterministic polynomial time (NP)-complete in nature, which were solved through the combined use of the analytical hierarchy process (AHP) algorithm and pruning techniques. For smart meters transmitting Internet Protocol (IP) packets of varying sizes at different intervals, the proposed technique considers the constraints of NAN and the applications’ traffic characteristics. The technique was developed by combining multiple OLSR path selection metrics with the AHP algorithminns-2. Compared with the conventional link metric in OLSR, the results show improvements of about 23% and 45% in latency and Packet Delivery Ratio (PDR), respectively, in a 25-node grid NAN.

An average U.K. electricity bill is made up of at least 60% service charge, with approximately 22% related to network characteristics including distance charge. This makes distance and network constraints important factors in matching prosumers on any peer-to-peer energy trading platform as assessed in this article. To realize that, a platform— $VirtElect$ , based on a double auction market is developed to support the matching interaction between prosumers. Case studies based on real microgrid data are used to verify the performance of the platform in demonstrating the potential of local energy consumption. The results show that it is possible to balance local energy generation and consumption, with little or no interaction with the utility grid. We also show that local energy trading is not only beneficial to the environment but also leads to a significant amount of cost savings of up to 45%, depending on the number of participants and their ratios on the platform.

The ripple effects of the pandemic have resulted in an unprecedented shift in sectoral energy consumption as the workforce predominantly stays and works from home. Quantifying the impact of these restrictions on energy consumption offers a new direction toward intelligent energy services in a post-coronavirus (post-COVID-19) world, especially for commercial buildings. Thus, utilizing actual power consumption data, the study evaluates how energy usage in commercial buildings can change in a post-COVID-19 world, while examining the impact of digitalization to identify potential new opportunities. The article analyzes the changes in energy demand with occupancy rate based on data from 126 commercial businesses with varied classes across Manchester, U.K. The results show that the reduction in energy demand is not proportionate to the occupancy level, resulting in high energy costs. For instance, an average footfall for February 2021 is 10% of 2020, while the costs of electricity only fall to 80% of 2020. Although most of the energy demand is from appliances, the absence of energy efficiency increases energy consumption, highlighting the urgent need for optimized energy efficiency measures to include the time of use and scheduled use of energy across people and processes.