MARTINI: Smart Meter Driven Estimation of HVAC Schedules and Energy Savings Based on WiFi Sensing and Clustering

• URL: http://arxiv.org/abs/2110.08927v1
• Date: Sun, 17 Oct 2021 21:41:33 GMT
• Title: MARTINI: Smart Meter Driven Estimation of HVAC Schedules and Energy Savings Based on WiFi Sensing and Clustering
• Authors: Kingsley Nweye and Zoltan Nagy
• Abstract summary: We propose a scalable way to estimate energy savings potential from energy conservation measures that is not limited by building parameters. We estimate the schedules by clustering WiFi-derived occupancy profiles and, energy savings by shifting ramp-up and setback times observed in typical/measured load profiles. Our case-study results with five buildings over seven months show an average of 8.1%-10.8% (summer) and 0.2%-5.9% (fall) chilled water energy savings when HVAC system operation is aligned with occupancy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: HVAC systems account for a significant portion of building energy use. Nighttime setback scheduling is an energy conservation measure where cooling and heating setpoints are increased and decreased respectively during unoccupied periods with the goal of obtaining energy savings. However, knowledge of a building's real occupancy is required to maximize the success of this measure. In addition, there is the need for a scalable way to estimate energy savings potential from energy conservation measures that is not limited by building specific parameters and experimental or simulation modeling investments. Here, we propose MARTINI, a sMARt meTer drIveN estImation of occupant-derived HVAC schedules and energy savings that leverages the ubiquity of energy smart meters and WiFi infrastructure in commercial buildings. We estimate the schedules by clustering WiFi-derived occupancy profiles and, energy savings by shifting ramp-up and setback times observed in typical/measured load profiles obtained by clustering smart meter energy profiles. Our case-study results with five buildings over seven months show an average of 8.1%-10.8% (summer) and 0.2%-5.9% (fall) chilled water energy savings when HVAC system operation is aligned with occupancy. We validate our method with results from building energy performance simulation (BEPS) and find that estimated average savings of MARTINI are within 0.9%-2.4% of the BEPS predictions. In the absence of occupancy information, we can still estimate potential savings from increasing ramp-up time and decreasing setback start time. In 51 academic buildings, we find savings potentials between 1%-5%.

Related papers

• Energy Optimization for HVAC Systems in Multi-VAV Open Offices: A Deep Reinforcement Learning Approach [4.323740171581589]
  HVAC systems account for about 40% of the total energy cost in the commercial sector. We propose a low-complexity DRL-based model with multi-input multi-output architecture for the HVAC energy optimization of open-plan offices.
  arXiv  Detail & Related papers  (2023-06-23T07:27:31Z)
• Low Emission Building Control with Zero-Shot Reinforcement Learning [70.70479436076238]
  Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency. We show it is possible to obtain emission-reducing policies without a priori--a paradigm we call zero-shot building control.
  arXiv  Detail & Related papers  (2022-08-12T17:13:25Z)
• 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)
• Uncertainty-Cognizant Model Predictive Control for Energy Management of Residential Buildings with PVT and Thermal Energy Storage [0.0]
  Building sector accounts for almost 40 percent of the global energy consumption. This paper offers a building energy system embracing a heat pump, a thermal energy storage system along with grid-connected thermal photovoltaic (PVT) collectors.
  arXiv  Detail & Related papers  (2022-01-21T22:30:13Z)
• LSTM-based Space Occupancy Prediction towards Efficient Building Energy Management [0.0]
  This paper proposes predictive time-series models of occupancy patterns using LSTM. Prediction signal for future room occupancy status on the next time span can be directly used to operate HVAC. We show that LSTM's room occupancy prediction based HVAC control could save energy usage by 50% compared to conventional RBC based control.
  arXiv  Detail & Related papers  (2020-12-15T06:32:07Z)
• 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)
• WattScale: A Data-driven Approach for Energy Efficiency Analytics of Buildings at Scale [2.771897351607068]
  Buildings consume over 40% of the total energy in modern societies. We present textttWattScale, a data-driven approach to identify the least energy-efficient buildings.
  arXiv  Detail & Related papers  (2020-07-02T20:45:33Z)
• Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids [9.208362060870822]
  We propose a model-free method for the households which works with limited information about the uncertain factors. We then utilize real-world data from Pecan Street Inc., which contains the power consumption profile of more than 1; 000 households. In average, the results reveal that we can achieve around 12% reduction on peak-to-average ratio (PAR) and 11% reduction on load variance.
  arXiv  Detail & Related papers  (2020-06-29T15:01:51Z)
• 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)
• 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.