Abrol et al., 2018 - Google Patents
Data-enabled building energy savings (DE BES)Abrol et al., 2018
View PDF- Document ID
- 5592381006905048702
- Author
- Abrol S
- Mehmani A
- Kerman M
- Meinrenken C
- Culligan P
- Publication year
- Publication venue
- Proceedings of the IEEE
External Links
Snippet
Building sector energy consumption represents a significant fraction of the overall energy consumption in urban communities. While there has been increasing focus on the development of smart environments to support energy savings in urban commercial …
- 230000000694 effects 0 abstract description 34
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
-
- 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
- G06Q30/0204—Market segmentation
- G06Q30/0205—Location or geographical consideration
-
- 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
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING ENGINES OR PUMPS
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING, AIR-HUMIDIFICATION, VENTILATION, USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety systems or apparatus
- F24F11/0009—Electrical control or safety systems or apparatus
- F24F11/001—Control systems or circuits characterised by their inputs, e.g. using sensors
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11561022B2 (en) | System and method for characterization of air leakage in building using data from communicating thermostats and/or interval meters | |
| Carpino et al. | Energy performance gap of a nearly Zero Energy Building (nZEB) in Denmark: The influence of occupancy modelling | |
| Haldi et al. | Modelling diversity in building occupant behaviour: a novel statistical approach | |
| Yin et al. | Quantifying flexibility of commercial and residential loads for demand response using setpoint changes | |
| Zhang et al. | An optimal and learning-based demand response and home energy management system | |
| Abrol et al. | Data-enabled building energy savings (DE BES) | |
| Fabi et al. | Influence of occupant's heating set-point preferences on indoor environmental quality and heating demand in residential buildings | |
| Gouveia et al. | Daily electricity consumption profiles from smart meters-Proxies of behavior for space heating and cooling | |
| Martinez et al. | Demand-side flexibility in a residential district: What are the main sources of uncertainty? | |
| Sandels et al. | Modeling office building consumer load with a combined physical and behavioral approach: Simulation and validation | |
| Parker et al. | A methodology for creating building energy model occupancy schedules using personal location metadata | |
| Aliero et al. | Systematic mapping study on energy optimization solutions in smart building structure: opportunities and challenges | |
| Lee et al. | The use of normative energy calculation beyond building performance rating systems | |
| Becker et al. | Estimating the savings potential of occupancy-based heating strategies | |
| Ueno et al. | A method to generate heating and cooling schedules based on data from connected thermostats | |
| Schuetz et al. | Automated modelling of residential buildings and heating systems based on smart grid monitoring data | |
| Chen et al. | Agent-based modeling and simulation of stochastic heat pump usage behavior in residential communities | |
| Patteeuw et al. | Clustering a building stock towards representative buildings in the context of air-conditioning electricity demand flexibility | |
| Baetens | On externalities of heat pump-based low-energy dwellings at the low-voltage distribution grid | |
| Shamsi et al. | A generalization approach for reduced order modelling of commercial buildings | |
| Li et al. | Preliminary study on heating energy consumption distribution of dwellings in hot summer and cold winter climate region of China | |
| Cipriano et al. | Influencing factors in energy use of housing blocks: A new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects | |
| Pazhoohesh et al. | Investigating occupancy-driven air-conditioning control based on thermal comfort level | |
| Kwak et al. | Management of cooling energy through building controls for thermal comfort and relative performance in an office building | |
| Swan | Residential sector energy and GHG emissions model for the assessment of new technologies |