XGBoost energy consumption prediction based on multi-system data HVAC

• URL: http://arxiv.org/abs/2105.09945v1
• Date: Thu, 20 May 2021 18:41:17 GMT
• Title: XGBoost energy consumption prediction based on multi-system data HVAC
• Authors: Yunlong Li, Yiming Peng, Dengzheng Zhang, Yingan Mai, Zhengrong Ruan
• Abstract summary: This paper extracts features from large data sets using XGBoost, trains them separately to obtain multiple models, then fuses them with LightGBM's independent prediction results using MAE. It successfully applies this model to the self-developed Internet of Things platform.
• Score: 0.2519906683279153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The energy consumption of the HVAC system accounts for a significant portion of the energy consumption of the public building system, and using an efficient energy consumption prediction model can assist it in carrying out effective energy-saving transformation. Unlike the traditional energy consumption prediction model, this paper extracts features from large data sets using XGBoost, trains them separately to obtain multiple models, then fuses them with LightGBM's independent prediction results using MAE, infers energy consumption related variables, and successfully applies this model to the self-developed Internet of Things platform.

Related papers

• 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)
• Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations [2.2765705959685234]
  This study investigates the energy consumption of Discriminative and Generative AI models within real-world MLOps pipelines. We employ software-based power measurements to ensure ease of replication across diverse configurations, models, and datasets.
  arXiv  Detail & Related papers  (2025-03-31T10:28:04Z)
• Just In Time Transformers [2.7350304370706797]
  JITtrans is a novel transformer deep learning model that significantly improves energy consumption forecasting accuracy. Our findings highlight the potential of advanced predictive technologies to revolutionize energy management and advance sustainable power systems.
  arXiv  Detail & Related papers  (2024-10-22T10:33:00Z)
• Deep Convolutional Neural Networks for Short-Term Multi-Energy Demand Prediction of Integrated Energy Systems [49.1574468325115]
  This paper develops six novel prediction models based on Convolutional Neural Networks (CNNs) for forecasting multi-energy power consumptions. The models are applied in a comprehensive manner on a novel integrated electrical, heat and gas network system.
  arXiv  Detail & Related papers  (2023-12-24T14:56:23Z)
• Utilizing Language Models for Energy Load Forecasting [11.670324826998968]
  We propose a novel approach that leverages language models for energy load forecasting. We employ prompting techniques to convert energy consumption data into descriptive sentences. Our results indicate that utilizing language models for energy load forecasting holds promise for enhancing energy efficiency.
  arXiv  Detail & Related papers  (2023-10-26T21:36:06Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• Deep Learning for Energy Time-Series Analysis and Forecasting [42.617834983479796]
  Energy time-series analysis describes the process of analyzing past energy observations and possibly external factors so as to predict the future. Following the exceptional performance of Deep Learning (DL) in a broad area of vision tasks, DL models have successfully been utilized in time-series forecasting tasks. This paper aims to provide insight into various DL methods geared towards improving the performance in energy time-series forecasting tasks.
  arXiv  Detail & Related papers  (2023-06-15T13:41:59Z)
• On Feature Diversity in Energy-based Models [98.78384185493624]
  An energy-based model (EBM) is typically formed of inner-model(s) that learn a combination of the different features to generate an energy mapping for each input configuration. We extend the probably approximately correct (PAC) theory of EBMs and analyze the effect of redundancy reduction on the performance of EBMs.
  arXiv  Detail & Related papers  (2023-06-02T12:30:42Z)
• Cascaded Deep Hybrid Models for Multistep Household Energy Consumption Forecasting [5.478764356647437]
  This study introduces two hybrid cascaded models for forecasting multistep household power consumption in different resolutions. The proposed hybrid models achieve superior prediction performance compared to the existing multistep power consumption prediction methods.
  arXiv  Detail & Related papers  (2022-07-06T11:02:23Z)
• Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction [3.7458346891274013]
  We use Weka, a data mining tool, to first apply models on hourly and daily household energy consumption datasets available from Kaggle data science community. Secondly, we also implemented time series forecasting models, ARIMA and VAR, in python to forecast household energy consumption of selected South Korean households with and without weather data. Our results show that the best methods for the forecasting of energy consumption prediction are Support Vector Regression followed by Multilayer Perceptron and Gaussian Process Regression.
  arXiv  Detail & Related papers  (2022-07-03T12:16:54Z)
• EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models [8.939420322774243]
  EnergyVis is an interactive energy consumption tracker for machine learning (ML) models. It enables researchers to interactively track, visualize and compare model energy consumption across key energy consumption and carbon footprint metrics. EnergyVis aims to raise awareness concerning computational sustainability by interactively highlighting excessive energy usage during model training.
  arXiv  Detail & Related papers  (2021-03-30T15:33:43Z)
• 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)
• 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.