Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors' knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table

What Ukraine Taught NATO about Hybrid Warfare

Russia’s invasion of Ukraine in 2022 forced the United States and its NATO partners to be confronted with the impact of hybrid warfare far beyond the battlefield. Targeting Europe’s energy security, Russia’s malign influence campaigns and malicious cyber intrusions are affecting global gas prices, driving up food costs, disrupting supply chains and grids, and testing US and Allied military mobility. This study examines how hybrid warfare is being used by NATO’s adversaries, what vulnerabilities in energy security exist across the Alliance, and what mitigation strategies are available to the member states. Cyberattacks targeting the renewable energy landscape during Europe’s green transition are increasing, making it urgent that new tools are developed to protect these emerging technologies. No less significant are the cyber and information operations targeting energy security in Eastern Europe as it seeks to become independent from Russia. Economic coercion is being used against Western and Central Europe to stop gas from flowing. China’s malign investments in Southern and Mediterranean Europe are enabling Beijing to control several NATO member states’ critical energy infrastructure at a critical moment in the global balance of power. What Ukraine Taught NATO about Hybrid Warfare will be an important reference for NATO officials and US installations operating in the European theater.https://press.armywarcollege.edu/monographs/1952/thumbnail.jp

K-Means and Alternative Clustering Methods in Modern Power Systems

As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies

Computational intelligence in extra low voltage direct currrent pico-grids

Ph. D. ThesisThe modern power system has gone through a lot of changes over the past few years. It is no longer about providing one-way power from sources to various loads. Power monitoring and management have become an increasingly essential task with the growing trend to provide users more information about the status of the loads within their energy consumption so that they can make an informed decision to reduce usage and cost or request desired maintenance. Computational intelligence has been successfully implemented in the electrical power systems to aid the user, but these research studies about this are generally conducted on the conventional alternative current (AC) macro-grids. Until now, little work has been done on direct current (DC) and the focus on smaller DC grids has been even less. In recent years, the evolution of electrical power system has seen the proliferation of direct current (DC) appliances and equipment such as buildings, households and office loads. This number keeps increasing with the advancement in technology and consumer lifestyles changes. Given that DC power supplies are getting more popular in the form of photovoltaic panels and batteries, it is possible for Extra Low Voltage (ELV) DC households or office pico-grids to come into use soon. This research recognises and addresses this research gap in the monitoring and managing of the DC picogrids. It recommends and applies the bottom-up monitoring and management approach in smaller scale grids and in larger scale grids. It innovatively categorises the loads in the grids into dumb loads that do not have intelligence and communication features and smart loads that have these features. While targeting at these ELV DC pico-grids, this research presents solutions that provide users useful information on load classification, load disaggregation, anomaly warning and early fault detection. It provides local and remote sensing with the alternative use of hardware to lessen the computational burden from the main computer. The inclusion of remote monitoring has opened a window of opportunities for Internet of Things (IoT) implementation. These solutions involve the blending of computational intelligence techniques with enhanced algorithms, such as K-Means algorithm, k-Nearest Neighbours (kNN) classification, Naïve Bayes Classification (NBC) Theorem, Statistical Process Control (SPC) and Long Short-Term Memory Recurrent Neural Network (LSTM RNN). As demonstrated in this research, these solutions produce high accuracy results in load classification and early anomaly detection in both AC and DC pico-grids. In addition to the load side, this research features a short-term PV energy forecasting technique that is easily comprehensible to users. This research contributes to the implementation of the Smart Grid with possible IoT features in DC pico-grids