- freely available
Future Internet 2017, 9(3), 29; doi:10.3390/fi9030029
- separate disaggregation data analyses from data representations;
- use open standard interfaces to exchange data (i.e., ZigBee and RESTful web interfaces) in a seamless way;
- provide a unique and shared database for appliance signatures and disaggregated results;
- improve the aggregated data quality by detecting and fixing its errors;
- combine the consumptions of several homes to detect appliance usage patterns.
- provide natural language reports about the user behavior to relevant stakeholders (hospitals, companies, etc.).
2. State of the Art
3.1. Quality Check
3.2. Gap Filling
3.3. Load Disaggregation Algorithm
3.4. Outcome Refinement
3.5. Data Representation
4. Proposed Framework
4.1. Data Quality Service
4.1.1. Sample availability
4.2. Gap Filling Service
4.2.1. Papoulis-Gerchberg Algorithm
4.2.2. Wiener Filling Algorithm
4.2.3. Spatio-Temporal Filling Algorithm
4.2.4. Envelope Filling Algorithm
4.2.5. Empirical Mode Decomposition Filling Algorithm
4.3. Load Disaggregation Algorithm (LDA)
4.3.1. Concepts and Challenges
- Type-1: Appliances that only have 2 states corresponding to ON/OFF. This could be a lamp or a water boiler.
- Type-2: These are appliances with multiple states. The appliance in this category can be modeled as a finite state machine. Many modern devices such as TV’s, computers and washing machines fall in this category.
- Type-3: These appliances are referred to as “Continuously Variable Devices”. These devices have a variable power draw and is impossible to model as a finite state machine. This could be appliances like power drills, and dimmer lights. These are by far the hardest for the NILM algorithms to detect.
- Type-4: These are a special kind of appliances that are always ON and consume energy at a constant rate. Such devices could be smoke detectors and burglary alarms.
4.3.3. Learning Strategy
4.3.5. Recognition Methods
Factorial Hidden Markov Models
4.4. Outcome Refinement Service
4.5. Data Representation Service
[let I: Sequence (InstanceSpecification) = model . eAllContents (InstanceSpecification)] [if (I. classifier ->at(i). name = ’User ’)] [I->at(i). name /] [let iUser : Integer = i] [for (it : NamedElement | I->at(iUser). clientDependency . supplier)] [it. name /] living in ..... [it. clientDependency . supplier . eAllContents (LiteralString).value ->sep(’from ’,’ to ’,’.’)/] ...
5. Experimental Validation
5.1. Quality Check
5.2. Data Recovery
5.2.1. Post and Prior Knowledge
5.2.2. SmartHG Dataset Reconstruction
5.3. Non-Intrusive Load Monitoring (NILM) Algorithms
5.4. Outcome Refinement
5.5. NLP and Report Generation
“Peter (User ID: 123) living in XYZ (Home ID: 2) was watching TV from 18:00 to 20:45 on 1 May 2015”.
“CompanyA (Service provider of Zone: 4321) ran algorithm named: Norm Filter on Smart Meters: 1, 2, 3, 4, 5 and detects that the TV was ON from 06:00 to 09:00 on Monday, 4 May 2015”.
Conflicts of Interest
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