Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time. NILM, or energy disaggregation, refers to the decompo...
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ISBN:
(纸本)9781665495127
Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time. NILM, or energy disaggregation, refers to the decomposition of electricity usage conditioned on the aggregated power signals (i.e., smart sensor on the main channel). Based on real-time appliance power prediction using sensory technology, energy disaggregation has great potential to increase electricity efficiency and reduce energy expenditure. Withthe introduction of transformer models, NILM has achieved significant improvements in predicting device power readings. Nevertheless, transformers are less efficient due to O(l2) complexity w.r.t. sequence length l. Moreover, transformers can fail to capture local signal patterns in sequenceto-point settings due to the lack of inductive bias in local context. In this work, we propose an efficient localness transformer for non-intrusive load monitoring (ELTransformer). Specifically, we leverage normalization functions and switch the order of matrix multiplication to approximate self-attention and reduce computational complexity. Additionally, we introduce localness modeling with sparse local attention heads and relative position encodings to enhance the model capacity in extracting short-term local patterns. To the best of our knowledge, ELTransformer is the first NILM model that addresses computational complexity and localness modeling in NILM. With extensive experiments and quantitative analyses, we demonstrate the efficiency and effectiveness of the the proposed ELTransformer with considerable improvements compared to state-of-the-art baselines.
Withthe continued progress of autonomous driving technology, the use of multi-sensor fusion assisted visual simultaneous localization and mapping (VSLAM) has emerged as a new research focus. However, when sensors sen...
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Withthe rapid development of artificial intelligence, research on its application in rural development is increasing. this paper focuses on the literature on artificial intelligence empowering rural and agricultural ...
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this study sets the task of designing a set of induction equipment and automated means of work infeed system to inductors using the theory of distributed parameter systems. the results of this research are obtained in...
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In this paper, we consider the problem of using a drone to collect information within orchards in order to detect bugs. An orchard can be modeled as an aisle-graph, which is a regular data structure formed by consecut...
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ISBN:
(纸本)9781665495127
In this paper, we consider the problem of using a drone to collect information within orchards in order to detect bugs. An orchard can be modeled as an aisle-graph, which is a regular data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone's energy is limited, only a subset of locations in the orchard can be visited with a fully charged battery. those places that are most likely to be infested should be selected to promptly detect the parasite. We study the budgeted constrained position selection problem in the orchard from an algorithmic point of view. We present the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the well-known orienteering problem where the finite resource is the drone's battery. We first show that SOAP can be optimally solved for aisle-graphs in polynomial time. However, the optimal solution is not efficient for large orchards. then, we propose two efficient heuristics that work even for large (orchard) instances. After a thorough analysis of the proposed solutions, we evaluate their performance by simulation experiments on both synthetic and real data sets.
Withthe development of network society, individuals and enterprises generate massive data every day. However, the traditional relational MySQL database has poor performance in storing and processing massive data. In ...
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By using the cloud computing platform that has achieved excellent commercial results to perform parallel classification processing of massive remote sensing data, it can meet the requirements of improving the parallel...
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this study presents a comprehensive study on smart home automation systems utilizing Internet of things (IoT) sensor technology to achieve efficient energy conservation. the proposed system integrates ZigBee and WiFi ...
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the proceedings contain 278 papers. the topics discussed include: layered control method for grid connected power consumption in electric hydrogen coupled energy systems;operation strategy study of wind-solar-hydrogen...
ISBN:
(纸本)9798350390315
the proceedings contain 278 papers. the topics discussed include: layered control method for grid connected power consumption in electric hydrogen coupled energy systems;operation strategy study of wind-solar-hydrogen-chemical integration system;research on energy-saving and emission reduction measures for cement kiln production process withthe introduction of clean energy;multi-time scale coordinated optimal scheduling for power system with a high proportion of new energy including multiple energy storage;optimization strategy for virtual energy storage of electric vehicles to participate in multiple ancillary services;optimal schedule of multi-energy co-generation with pumped storage power station based on particle swarm optimization;evaluation of the coordination between new energy integration and regional power grid;and static voltage stability based maximum integration ratio of distributed renewable energy in distribution power system.
In recent years, the 'whole chain' development level of China's intellectual property creation, protection and application has been greatly improved. At the same time, cloud computing technology is booming...
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