[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Wang et al., 2022 - Google Patents

Data-driven assessment of room air conditioner efficiency for saving energy

Wang et al., 2022

Document ID
3594421093063731884
Author
Wang W
Zhou Z
Lu Z
Publication year
Publication venue
Journal of Cleaner Production

External Links

Snippet

Room air conditioners (RACs) are one of the high energy-consuming home appliances. Developing a smart solution to evaluate and track the efficiency of RACs is essentially useful for residents to make necessary maintenance or replacement decisions. While smart meters …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G06Q10/0639—Performance analysis
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce, e.g. shopping or e-commerce
    • G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202—Market predictions or demand forecasting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric

Similar Documents

Publication Publication Date Title
Pinto et al. Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives
Lu et al. GMM clustering for heating load patterns in-depth identification and prediction model accuracy improvement of district heating system
Grillone et al. A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings
Amasyali et al. A review of data-driven building energy consumption prediction studies
Gunay et al. Data analytics to improve building performance: A critical review
Liu et al. Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network
Chae et al. Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings
Zeng et al. Comparative study of data driven methods in building electricity use prediction
Chou et al. Hybrid machine learning system to forecast electricity consumption of smart grid-based air conditioners
Chen et al. Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms
Xu et al. Prediction of thermal energy inside smart homes using IoT and classifier ensemble techniques
Do et al. Residential building energy consumption: a review of energy data availability, characteristics, and energy performance prediction methods
Ahmad et al. Novel deep supervised ML models with feature selection approach for large-scale utilities and buildings short and medium-term load requirement forecasts
Wang et al. Data-driven assessment of room air conditioner efficiency for saving energy
Zaeri et al. Disaggregation of electricity and heating consumption in commercial buildings with building automation system data
Sun et al. Optimization of support vector regression model based on outlier detection methods for predicting electricity consumption of a public building WSHP system
Gunay et al. Disaggregation of commercial building end-uses with automation system data
Amasyali et al. Hybrid approach for energy consumption prediction: Coupling data-driven and physical approaches
Touretzky et al. Building-level power demand forecasting framework using building specific inputs: Development and applications
Yu et al. Estimating power demand shaving capacity of buildings on an urban scale using extracted demand response profiles through machine learning models
Yan et al. Predicting household air conditioners’ on/off state considering occupants’ preference diversity: A study in Chongqing, China
Liu et al. Improvement of the energy evaluation methodology of individual office building with dynamic energy grading system
Khan et al. Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism
Runge et al. Deep learning forecasting for electric demand applications of cooling systems in buildings
Wang et al. Investigating the deviation between prediction accuracy metrics and control performance metrics in the context of an ice-based thermal energy storage system