[HTML][HTML] 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 …
installed in residential buildings. If leveraged properly, that data could assist end-users …
Anomaly detection for IoT time-series data: A survey
Anomaly detection is a problem with applications for a wide variety of domains; it involves
the identification of novel or unexpected observations or sequences within the data being …
the identification of novel or unexpected observations or sequences within the data being …
Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges
Anomaly detection has recently been applied to various areas, and several techniques
based on deep learning have been proposed for the analysis of multivariate time series. In …
based on deep learning have been proposed for the analysis of multivariate time series. In …
Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders
Energy consumption in buildings is increasing because of social development and
urbanization. Forecasting the energy consumption in buildings is essential for improving …
urbanization. Forecasting the energy consumption in buildings is essential for improving …
Dynamic adaptive encoder-decoder deep learning networks for multivariate time series forecasting of building energy consumption
Accurate energy consumption prediction models can bring tremendous benefits to building
energy efficiency, where the use of data-driven models allows models to be trained based …
energy efficiency, where the use of data-driven models allows models to be trained based …
A novel approach for detecting anomalous energy consumption based on micro-moments and deep neural networks
Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of
householders are among the principal challenges in identifying ways to reduce power …
householders are among the principal challenges in identifying ways to reduce power …
[HTML][HTML] An ensemble learning framework for anomaly detection in building energy consumption
During building operation, a significant amount of energy is wasted due to equipment and
human-related faults. To reduce waste, today's smart buildings monitor energy usage with …
human-related faults. To reduce waste, today's smart buildings monitor energy usage with …
Drift-aware methodology for anomaly detection in smart grid
Energy efficiency and sustainability are important factors to address in the context of smart
cities. In this sense, smart metering and nonintrusive load monitoring play a crucial role in …
cities. In this sense, smart metering and nonintrusive load monitoring play a crucial role in …
A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings
Measured and simulated data sources from the built environment are increasing rapidly. It is
becoming normal to analyze data from hundreds, or even thousands of buildings at once …
becoming normal to analyze data from hundreds, or even thousands of buildings at once …
Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns
Smart grids are a promising solution to the rapidly growing power demand because they can
considerably increase building energy efficiency. This study developed a novel time-series …
considerably increase building energy efficiency. This study developed a novel time-series …