Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring
This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings. The paper aims to improve the accuracy of NILM by using a deep learning-based method. The proposed method uses a Seq2Seq model with a transformer-based attention mechanism to capture the long-term dependencies of NILM data. Additionally, temporal pooling is used to improve the model's accuracy by capturing both the steady-state and transient behavior of appliances. The paper evaluates the proposed method on a publicly available dataset and compares the results with other state-of-the-art NILM techniques. The results demonstrate that the proposed method outperforms the existing methods in terms of both accuracy and computational efficiency.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencyNon-Intrusive Load MonitoringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
ST-DETR: Spatio-Temporal Object Traces Attention Detection Transformer
We propose ST-DETR, a Spatio-Temporal Transformer-based architecture for object detection from a sequence of temporal frames. We treat the temporal frames as sequences in both space and time and employ the full attention…
Moving Object DetectionObjectobject-detectionObject DetectionTransformers and Cortical Waves: Encoders for Pulling In Context Across Time
The capabilities of transformer networks such as ChatGPT and other Large Language Models (LLMs) have captured the world's attention. The crucial computational mechanism underlying their performance relies on transforming…
SentencePRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting
The self-attention mechanism in Transformer architecture, invariant to sequence order, necessitates positional embeddings to encode temporal order in time series prediction. We argue that this reliance on positional embe…
Multivariate Time Series ForecastingTemporal SequencesTime SeriesTime Series Forecasting+1STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition
We study the problem of human action recognition using motion capture (MoCap) sequences. Unlike existing techniques that take multiple manual steps to derive standardized skeleton representations as model input, we propo…
Action RecognitionTemporal Action LocalizationDRAformer: Differentially Reconstructed Attention Transformer for Time-Series Forecasting
Time-series forecasting plays an important role in many real-world scenarios, such as equipment life cycle forecasting, weather forecasting, and traffic flow forecasting. It can be observed from recent research that a va…
DecoderTime SeriesTime Series AnalysisTime Series Forecasting+1