Batbaatar et al., 2018 - Google Patents
DeepEnergy: Prediction of appliances energy with long-short term memory recurrent neural networkBatbaatar et al., 2018
View PDF- Document ID
- 14671339534521635125
- Author
- Batbaatar E
- Park H
- Li D
- Li M
- Ryu K
- Publication year
- Publication venue
- Asian Conference on Intelligent Information and Database Systems
External Links
Snippet
Our world is becoming more interconnected and intelligent, huge amount of data has been generated newly. Home appliances' energy usage is the basis of home energy management and highly depends on weather condition and environment. Using weather in context, it is …
- 230000001537 neural 0 title abstract description 24
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
-
- 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
- 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
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- 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 |
---|---|---|
Dubey et al. | Study and analysis of SARIMA and LSTM in forecasting time series data | |
Li et al. | A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction | |
Mawson et al. | Deep learning techniques for energy forecasting and condition monitoring in the manufacturing sector | |
Amasyali et al. | A review of data-driven building energy consumption prediction studies | |
Yildiz et al. | Recent advances in the analysis of residential electricity consumption and applications of smart meter data | |
Su et al. | A systematic data-driven Demand Side Management method for smart natural gas supply systems | |
Khafaga et al. | Forecasting energy consumption using a novel hybrid dipper throated optimization and stochastic fractal search algorithm | |
Xu et al. | Prediction of thermal energy inside smart homes using IoT and classifier ensemble techniques | |
Marino et al. | A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics | |
Antonopoulos et al. | Data-driven modelling of energy demand response behaviour based on a large-scale residential trial | |
Xie et al. | Does historical data still count? Exploring the applicability of smart building applications in the post-pandemic period | |
Jahani et al. | City-scale single family residential building energy consumption prediction using genetic algorithm-based numerical moment matching technique | |
Wang et al. | Artificial intelligent models for improved prediction of residential space heating | |
Sengar et al. | Ensemble approach for short term load forecasting in wind energy system using hybrid algorithm | |
Fakhar et al. | A survey of smart home energy conservation techniques | |
Jha et al. | Electricity load forecasting and feature extraction in smart grid using neural networks | |
Lin et al. | A smart home energy management system utilizing neurocomputing-based time-series load modeling and forecasting facilitated by energy decomposition for smart home automation | |
Alhendi et al. | Short-term load and price forecasting using artificial neural network with enhanced Markov chain for ISO New England | |
Batbaatar et al. | DeepEnergy: Prediction of appliances energy with long-short term memory recurrent neural network | |
Waseem et al. | Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors | |
Atef et al. | A deep bi-directional long-short term memory neural network-based methodology to enhance short-term electricity load forecasting for residential applications | |
Sha et al. | Development of a multi-granularity energy forecasting toolkit for demand response baseline calculation | |
Panda et al. | Analysis and evaluation of two short-term load forecasting techniques | |
Dagnely et al. | Predicting hourly energy consumption. Can regression modeling improve on an autoregressive baseline? | |
Adams et al. | Data-driven simulation for energy consumption estimation in a smart home |