Runge et al., 2022 - Google Patents
Deep learning forecasting for electric demand applications of cooling systems in buildingsRunge et al., 2022
- Document ID
- 14585605360075753792
- Author
- Runge J
- Zmeureanu R
- Publication year
- Publication venue
- Advanced Engineering Informatics
External Links
Snippet
This paper presents the application of a deep learning based model for the short-term forecasting of the electric demand in a heating, ventilation, and air conditioning system (HVAC) for the demand response programs of utility companies. The deep learning model is …
- 238000001816 cooling 0 title description 53
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
- 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
- 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
- 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
-
- 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
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Mawson et al. | Deep learning techniques for energy forecasting and condition monitoring in the manufacturing sector | |
Luo et al. | Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings | |
Ahmad et al. | Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment | |
Hu et al. | Thermal load prediction and operation optimization of office building with a zone-level artificial neural network and rule-based control | |
Ma et al. | Statistical analysis of energy consumption patterns on the heat demand of buildings in district heating systems | |
Karatasou et al. | Modeling and predicting building's energy use with artificial neural networks: Methods and results | |
Zhao et al. | A review on the prediction of building energy consumption | |
Monfet et al. | Development of an energy prediction tool for commercial buildings using case-based reasoning | |
O’Neill et al. | Development of a probabilistic graphical model for predicting building energy performance | |
Hahn et al. | Electric load forecasting methods: Tools for decision making | |
Ahmad et al. | Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management | |
Chou et al. | Hybrid machine learning system to forecast electricity consumption of smart grid-based air conditioners | |
Yalcintas | Energy-savings predictions for building-equipment retrofits | |
Xuemei et al. | Building cooling load forecasting model based on LS-SVM | |
Do et al. | Residential building energy consumption: a review of energy data availability, characteristics, and energy performance prediction methods | |
Jafari et al. | Improving building energy footprint and asset performance using digital twin technology | |
Asare-Bediako et al. | Day-ahead residential load forecasting with artificial neural networks using smart meter data | |
Kim et al. | Sequence-to-sequence deep learning model for building energy consumption prediction with dynamic simulation modeling | |
Ozoh et al. | A comparative analysis of techniques for forecasting electricity consumption | |
Runge et al. | Deep learning forecasting for electric demand applications of cooling systems in buildings | |
Popescu et al. | Simulation models for the analysis of space heat consumption of buildings | |
Pedersen | Load modelling of buildings in mixed energy distribution systems | |
Sha et al. | Development of a multi-granularity energy forecasting toolkit for demand response baseline calculation | |
Zolfaghari et al. | A hybrid approach to model and forecast the electricity consumption by NeuroWavelet and ARIMAX-GARCH models | |
Maki et al. | Employing electricity-consumption monitoring systems and integrative time-series analysis models: A case study in Bogor, Indonesia |