Low-Cost HEM with Arduino and Zigbee Technologies in the Energy Sector in Colombia

• URL: http://arxiv.org/abs/2311.14767v1
• Date: Fri, 24 Nov 2023 10:19:21 GMT
• Title: Low-Cost HEM with Arduino and Zigbee Technologies in the Energy Sector in Colombia
• Abstract summary: This paper describes the design and implementation of a low-cost home energy management system (HEMS) The evaluation has shown that with the installation of this HEMS, consumption is reduced by 27%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since no solutions have been proposed in Colombia that seek to reduce the consumption of electricity at the residential level, this paper describes the design and implementation of a simple prototype of a low-cost home energy management system (HEMS). The objective of this plat-form is to monitor the energy consumption of typical household devices so that users can access the consumption of each device separately and then establish the strategy that allows them to reduce energy consumption at home. In order to demonstrate that our system is viable, the system has been evaluated by measuring weekly energy consumption with the on-line and off-line HEMS using a test bench with typical household devices in a Sincelejo typical household. The evaluation has shown that with the installation of this HEMS, consumption is reduced by 27%. This shows that it is possible to achieve a good reduction percentage with a low-cost system.

Related papers

• Hidden Degradation Costs in Energy-Cost-Only HEMS Optimisation: Study on Battery and PV Sensitivity [0.0]
  Battery energy storage systems (BESS) are increasingly deployed alongside photovoltaic (PV) generation to reduce household energy costs under volatile time-of-use (TOU) tariffs.<n>Model predictive control (MPC) is a widely adopted optimisation strategy for home energy management systems (HEMS), typically formulated to minimise net energy cost.<n>However, battery degradation is rarely embedded in the optimisation objective, meaning its cost is unquantified and aggressive.<n>This paper presents a receding-horizon mixed-integer linear programming (MILP) baseline for a UK residential HEMS, using demand data from the REFIT dataset.
  arXiv  Detail & Related papers  (2026-06-14T22:52:32Z)
• A Reinforcement Learning Approach for Optimal Control in Microgrids [43.122212629962235]
  Microgrids provide a promising solution by enabling localized control over energy generation, storage, and distribution.<n>This paper presents a novel reinforcement learning (RL)-based methodology for optimizing microgrid energy management.
  arXiv  Detail & Related papers  (2025-06-28T20:10:00Z)
• Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series [10.862097756793574]
  CamAL is a weakly supervised approach for appliance pattern localization that only requires information on the presence of an appliance in a household to be trained.<n>Our experimental evaluation, conducted on 4 real-world datasets, demonstrates that CamAL significantly outperforms existing weakly supervised baselines.
  arXiv  Detail & Related papers  (2025-06-06T09:08:34Z)
• Intelligent Duty Cycling Management and Wake-up for Energy Harvesting IoT Networks with Correlated Activity [43.00680041385538]
  This paper presents an approach for energy-neutral Internet of Things (IoT) scenarios where the IoT devices rely entirely on their energy harvesting capabilities to sustain operation. We use a Markov chain to represent the operation and transmission states of the IoTDs, a modulated Poisson process to model their energy harvesting process, and a discrete-time Markov chain to model their battery state. We propose a duty-cycling management based on K- nearest neighbors, aiming to strike a trade-off between energy efficiency and detection accuracy.
  arXiv  Detail & Related papers  (2024-05-10T10:16:27Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater [68.8204255655161]
  We present an unsupervised low-frequency method aimed at detecting and disaggregating the power used by Cumulative Water Heaters (CWH) in residential homes. Our model circumvents the inherent difficulty of unsupervised signal disaggregation by using both the shape of a power spike and its time of occurrence. Our model, despite its simplicity, offers promising applications: detection of mis-configured CWHs on off-peak contracts and slow performance degradation.
  arXiv  Detail & Related papers  (2022-03-22T10:39:19Z)
• Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring [81.06807079998117]
  We propose a novel approach to enable Automated Machine Learning (AutoML) for Non-Intrusive Appliance Load Monitoring (NIALM) NIALM offers a cost-effective alternative to smart meters for measuring the energy consumption of electric devices and appliances.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• ECO: Enabling Energy-Neutral IoT Devices through Runtime Allocation of Harvested Energy [0.8774604259603302]
  We present a runtime-based energy-allocation framework to optimize the utility of the target device under energy constraints. The proposed framework uses an efficient iterative algorithm to compute initial energy allocations at the beginning of a day. We evaluate this framework using solar and motion energy harvesting modalities and American Time Use Survey data from 4772 different users.
  arXiv  Detail & Related papers  (2021-02-26T17:21:25Z)
• Appliance Operation Modes Identification Using Cycles Clustering [3.328276101150529]
  Internet of Things (IoT) and Machine Learning (ML) deliver techniques that have the potential to efficiently conserve energy. SHEMSs have the potential to contribute in energy conservation through the application of Demand Response (DR) in the residential sector.
  arXiv  Detail & Related papers  (2021-01-25T23:25:45Z)
• Bayesian model of electrical heating disaggregation [68.8204255655161]
  Adoption of smart meters is a major milestone on the path of European transition to smart energy. The residential sector in France represents $approx$35% of electricity consumption with $approx$40% (INSEE) of households using electrical heating. The number of deployed smart meters Linky is expected to reach 35M in 2021.
  arXiv  Detail & Related papers  (2020-11-11T10:05:15Z)
• Reshaping consumption habits by exploiting energy-related micro-moment recommendations: A case study [2.741120981602367]
  This work builds on the detection of repeated usage patterns from consumption logs. It presents the structure and operation of an energy consumption reduction system, which employs a set of sensors, smart-meters and actuators. The system recommends to the user the proper energy saving action at the right moment and gradually shapes user's habits.
  arXiv  Detail & Related papers  (2020-10-09T17:29:56Z)
• A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids [58.666456917115056]
  This paper presents a Reinforcement Learning based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and improves the economic benefits for prosumers.
  arXiv  Detail & Related papers  (2020-09-23T02:17:51Z)
• A Hierarchical Approach to Multi-Energy Demand Response: From Electricity to Multi-Energy Applications [1.5084441395740482]
  This paper looks into an opportunity to control energy consumption of an aggregation of many residential, commercial and industrial consumers. This ensemble control becomes a modern demand response contributor to the set of modeling tools for multi-energy infrastructure systems.
  arXiv  Detail & Related papers  (2020-05-05T17:17:51Z)
• Energy Disaggregation with Semi-supervised Sparse Coding [0.0]
  Energy disaggregation research aims to decompose the aggregated energy consumption data into its component appliances. In this paper, a discriminative disaggregation model based on sparse coding has been evaluated on large-scale household power usage dataset for energy conservation.
  arXiv  Detail & Related papers  (2020-04-20T21:05:25Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

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.