Machine learning methods for modelling and analysis of time series signals in geoinformatics

• URL: http://arxiv.org/abs/2109.09499v1
• Date: Thu, 16 Sep 2021 16:18:13 GMT
• Title: Machine learning methods for modelling and analysis of time series signals in geoinformatics
• Authors: Maria Kaselimi
• Abstract summary: This dissertation evaluates the performance of several deep learning (DL) architectures on a large number of time series datasets of different nature and for different applications. The first problem is related to ionospheric Total Content (TEC) modeling which is an important issue in many real time Global Navigation System Satellites (GNSS) applications. The next problem is energy disaggregation which is an important issue for energy efficiency and energy consumption awareness.
• Score: 2.193013035690221
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this dissertation is provided a comparative analysis that evaluates the performance of several deep learning (DL) architectures on a large number of time series datasets of different nature and for different applications. Two main fruitful research fields are discussed here which were strategically chosen in order to address current cross disciplinary research priorities attracting the interest of geodetic community. The first problem is related to ionospheric Total Electron Content (TEC) modeling which is an important issue in many real time Global Navigation System Satellites (GNSS) applications. Reliable and fast knowledge about ionospheric variations becomes increasingly important. GNSS users of single frequency receivers and satellite navigation systems need accurate corrections to remove signal degradation effects caused by the ionosphere. Ionospheric modeling using signal processing techniques is the subject of discussion in the present contribution. The next problem under discussion is energy disaggregation which is an important issue for energy efficiency and energy consumption awareness. Reliable and fast knowledge about residential energy consumption at appliance level becomes increasingly important nowadays and it is an important mitigation measure to prevent energy wastage. Energy disaggregation or Nonintrusive load monitoring (NILM) is a single channel blind source separation problem where the task is to estimate the consumption of each electrical appliance given the total energy consumption. For both problems various deep learning models (DL) are proposed that cover various aspects of the problem under study, whereas experimental results indicate the proposed methods superiority compared to the current state of the art.

Related papers

• A Gap in Time: The Challenge of Processing Heterogeneous IoT Point Data in Buildings [15.06538531625261]
  The need for sustainable energy solutions has driven the integration of digitalized buildings into the power grid. incorporating IoT point data within deep-learning frameworks for energy management presents a complex challenge. This paper comprehensively analyzes the multifaceted heterogeneity present in real-world building IoT data streams.
  arXiv  Detail & Related papers  (2024-05-23T07:45:48Z)
• An Energy-Efficient Ensemble Approach for Mitigating Data Incompleteness in IoT Applications [0.0]
  It is important to build IoT-based Machine Learning systems that are robust against data incompleteness while simultaneously being energy efficient. ENAMLE is a proactive, energy-aware technique for mitigating the impact of concurrent missing data. We present extensive experimental studies on two distinct datasets that demonstrate the energy efficiency of ENAMLE.
  arXiv  Detail & Related papers  (2024-03-15T15:01:48Z)
• A Scoping Review of Energy Load Disaggregation [1.6783315930924723]
  Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management. The field currently lacks a comprehensive overview. Domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed.
  arXiv  Detail & Related papers  (2024-01-10T09:59:12Z)
• ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation [43.684035409535696]
  Existing imputation solutions mainly include low-rank models and deep learning models. We demonstrate a low rankness-induced bias balance between strong inductive bias and hightemporal model expressivity. We demonstrate its superiority in terms of accuracy, efficiency, and versatility in heterogeneous datasets, including traffic flow, solar energy, smart meters and air quality.
  arXiv  Detail & Related papers  (2023-12-04T08:35:31Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models [0.0]
  Machine learning methods have been adopted in the literature as contenders to conventional methods to solve the energy time series forecasting (TSF) problems. Yet, the evidence about their performance in to solve the energy TSF problems, in terms of accuracy and computational requirements, is scanty. This paper provides a comprehensive analytical assessment for conventional,machine learning, and deep learning methods that can be utilized to solve various energy TSF problems.
  arXiv  Detail & Related papers  (2021-08-24T12:02:26Z)
• Adversarial Energy Disaggregation for Non-intrusive Load Monitoring [78.47901044638525]
  Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions. Recent advances reveal that deep neural networks (DNNs) can get favorable performance for NILM. We introduce the idea of adversarial learning into NILM, which is new for the energy disaggregation task.
  arXiv  Detail & Related papers  (2021-08-02T03:56:35Z)
• Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
  Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns. This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
  arXiv  Detail & Related papers  (2021-02-16T16:56:27Z)
• Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space [62.39318039798564]
  We propose a scheduling mechanism capable of taking advantage of correlated information. The proposed mechanism is capable of determining the frequency with which sensors should transmit their updates. We show that our solution can significantly extend the sensors' lifetime.
  arXiv  Detail & Related papers  (2020-11-19T09:53:27Z)
• Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections [1.7503398807380832]
  We present auxiliary informed-temporal neural architecture for statistical downscaling. Current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees (115 km) to 0.25 degrees (25 km) over the world's most climatically diversified country, India.
  arXiv  Detail & Related papers  (2020-09-23T17:52:09Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.