Related papers: Active Collaborative Sensing for Energy Breakdown
The growing demand for intelligent applications beyond the network edge, coupled with the need for sustainable operation, are driving the seamless integration of deep learning (DL) algorithms into energy-limited, and even energy-harvesting…
Many residential prosumers exhibit a high price-tolerance for household electricity bills and a low response to price incentives. This is because the household electricity bills are not inherently high, and the potential for saving on…
The energy consumption of private households amounts to approximately 30% of the total global energy consumption, causing a large share of the CO2 emissions through energy production. An intelligent demand response via load shifting…
Greenhouse gas emissions from the residential sector represent a significant fraction of global emissions. Governments and utilities have designed incentives to stimulate the adoption of decarbonization technologies such as rooftop PV and…
A major challenge to implementing residential demand response is that of aligning the objectives of many households, each of which aims to minimize its payments and maximize its comfort level, while balancing this with the objectives of an…
Data-driven soft sensors are extensively used in industrial and chemical processes to predict hard-to-measure process variables whose real value is difficult to track during routine operations. The regression models used by these sensors…
Energy theft constitutes an issue of great importance for electricity operators. The attempt to detect and reduce non-technical losses is a challenging task due to insufficient inspection methods. With the evolution of advanced metering…
Smart sensing is expected to become a pervasive technology in smart cities and environments of the near future. These services are improving their capabilities due to integrated devices shrinking in size while maintaining their…
Non-intrusive presence detection of individuals in commercial buildings is much easier to implement than intrusive methods such as passive infrared, acoustic sensors, and camera. Individual power consumption, while providing useful feedback…
This paper presents an intelligent home energy management system integrated with dispatchable loads (e.g., clothes washers and dryers), distributed renewable generators (e.g., roof-top solar panels), and distributed energy storage devices…
Traditional energy-based learning models associate a single energy metric to each configuration of variables involved in the underlying optimization process. Such models associate the lowest energy state to the optimal configuration of…
In this work, a method for unsupervised energy disaggregation in private households equipped with smart meters is proposed. This method aims to classify power consumption as active or passive, granting the ability to report on the…
Energy consumption for hot water production is a major draw in high efficiency buildings. Optimizing this has typically been approached from a thermodynamics perspective, decoupled from occupant influence. Furthermore, optimization usually…
Cognitive radio and dynamic spectrum access represent a new paradigm shift in more effective use of limited radio spectrum. One core component behind dynamic spectrum access is the sensing of primary user activity in the shared spectrum.…
We investigate active learning in the context of deep neural network models for change detection and map updating. Active learning is a natural choice for a number of remote sensing tasks, including the detection of local surface changes:…
With the rise of AI in recent years and the increase in complexity of the models, the growing demand in computational resources is starting to pose a significant challenge. The need for higher compute power is being met with increasingly…
Energy-poor households often compromise their thermal comfort and refrain from operating mechanical cooling devices to avoid high electricity bills. This is compounded by certain behavioral practices like retention of older, less efficient…
In response to the substantial energy consumption in buildings, the Japanese government initiated the BI-Tech (Behavioral Insights X Technology) project in 2019, aimed at promoting voluntary energy-saving behaviors through the utilization…
Computer science marches towards energy-aware practices. This trend impacts not only the design of computer architectures, but also the design of programs. However, developers still lack affordable and accurate technology to measure energy…
Fixing energy leakage caused by different anomalies can result in significant energy savings and extended appliance life. Further, it assists grid operators in scheduling their resources to meet the actual needs of end users, while helping…