Literature Review: Simulation of power signatures for appliances

Since data collection (including ground truth) is not a trivial process in NILM, very few standard datasets exist that can be used for validation of algorithms. Oliver Parson has a good list of publicly available datasets for NILM here. One way to get around the necessity of datasets is to simulate  a reaslistic one. In this post, I review the proposed literature aimed at modelling appliance-level and whole home power consumption patterns.

Authors from UMASS: This is probably the most comprehensive simulator of whole home power consumption based on appliance level modeling (and the only one that doesn’t assume appliances consume power in a stepwise fashion). They model three types of loads: resistive (as simple step functions), inductive (as exponential decay), and capacitive (as min-max models with exponentially distributed spikes). They also have complex loads that are combination of one or more of these models. Although they haven’t analyzed in detail how individual power traces can be combined realistically to get an whole home power consumption signature, I assume probabilistic models of when appliance are likely to operate (based on a house’s characteristics like number of occupants, geographic location, etc.) will come into play. A dataset like Pecan street‘s or ICF‘s* would be invaluable in building a realistic usage model from the appliance models in this paper.

*ICF was the implementer (and needs to be contacted for the dataset). The UK Government carried out the study.

Researchers at King’s College do use the patterns from the UK (/ICF) dataset to simulate whole home electricity consumption, but their model only forecasts at an hourly level (and most load signatures aren’t available at that resolution). Something very similar has been done by researchers at University of Waterloo using a similar dataset and similar premise to create hourly level data.

Researchers at Loughborough University add information from occupancy models (based on certain TOS surveys), activity simulation, in addition to building appliance-level simulated data to create whole home consumption profiles at a minute level granularity. They use data from 22 houses at the same resolution to validate their simulation models. Their appliance modeling part is fairly weak, where most appliances are modelled as two state, and revolve around fixed parameters (average annual usage, assigned power factor etc). From a disaggregation stand-point this might not be of much use, although from a perspective of demand forecasting, this might be useful.

Authors from Korea University and Samsung have created a simulator based on SystemC platform (typically used for simulation in design automation) that operates at granularity of a second. They use a user-module to model a user’s behavior for turning appliances on and off, and an appliance-module to model an appliance’s operation behavior. They use hardcoded values to model an appliance’s power consumption, and different states that it can exist in. Again, power is modelled as step changes, and it is not clear how user behavior is modelled. For the test bed they have, they appear to have used the exact time the appliance was switiching states, and the power consumption in each states to model the behavior. Obviously comparing the “model” using these parameters will yield perfect results. This model isn’t as powerful as a model that does incorporates user behavior to simulate the operation of different appliances.

iPower: iPower is a collaborative platform (of companies and universities). The report is an attempt to model common household loads using Matlab’s simulink platform, to simulate whole building consumption. They see this as beneficial in formalizing control problems. HVAC consumption is modelled as a markov model with parameters like indoor temp, outdoor temp, thermal resistance between walls, thermal capacity. (Actually they estimate the tempearture inside the room, and assume HVAC will be a function of that). Similarly, they model a fridge’s consumption based on temperature differentials,and other paratmers like specific heat, cold storage mass, etc. Generic appliances (like dishwasher, microwave etc) are just modelled based on prior assumptions and hardcoded values.

Vienna University of Technology: This is a more interactive simulation. It models appliances as step changes. It asks for values like maximum allowed power, earliest time of use, latest time of use, and creates power consumption signatures. The selection of appliances are few, and the overall building consumption hasn’t been discussed.

Osaka University: In this model, the annual energy consumption of a house is simulated using schedule of living activities, weather data and energy efficiencies of appliances and buildings. They base their simulation off a survey of activities, and model appliances as those linked to occupancy/activity and those that are not. Fridge model is built using outside air temp, water heater using variables like city water temp, outside air temp etc., HVAC consumption model is built using some assumptions about physical properties of buildings, lighting is simulated using floor area. The operating power values and standby power values are hardcoded for ~16 appliances. (They take these micro models to estimate city wide power consumption at hourly level.)

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