1. Field of the Invention
The present invention relates to methods for monitoring the electrical load within a building, and, more particularly, to methods for monitoring the electrical load status within a building in a non-intrusive way.
2. Description of the Related Art
The price and consumption of energy throughout the world has been increasing dramatically over recent years and is expected to continue along this trend in the years to come. For example, according to the U.S. Department of Energy Annual Energy Outlook, total residential energy consumption is expected to increase by approximately twenty percent from 2007 to 2030 despite efficiency improvements. This is attributed to a number of factors including a projected twenty-four percent increase in the number of households and an approximately seven percent increase in the share of electricity attributed to “other” appliances such as home electronics. Increases in residential electricity consumption are accompanied by a projected 1.4 percent increase per year in commercial electricity consumption. Given these figures, and the fact that residential and commercial buildings comprise the largest energy consumer segment in the U.S., accounting for seventy-two percent of U.S. electricity consumption and forty percent of all energy use in the U.S., the recent push for technological solutions that increase energy awareness and efficiency are of no surprise.
To achieve energy efficiency goals, appliances or devices can be replaced with more energy efficient alternatives, building occupants or owners can alter their behavior to reduce the use of energy-consuming devices, or automated building management solutions can control the operation of devices in the building so as to achieve less energy consumption or schedule operation for non-peak demand periods to reduce energy costs. All of these approaches must be based on detailed knowledge of the amount of energy consumed by each device currently in the building and its periods of operation so that appropriate decisions can be made about how to reduce this consumption. Measurement of energy consumption may be called for in order to make decision-makers aware of patterns of past energy consumption which may carry over into the future. Such decision-makers may be human or automated.
A number of systems exist for measuring energy consumption in a building and reporting this to users. However, these systems typically report to the user only the total amount of electricity consumption for the entire building. In order to obtain truly detailed information that is most informative for determining how to achieve energy savings, the user must manually switch devices on and off and note the change in the total consumption report. Most current systems lack any disaggregated reporting of the consumption and use patterns of individual devices and appliances. Some exceptions are systems that use separate measurement devices to measure the electricity consumption of, for example, a sub-circuit of the building, an individual wall outlet, or even an individual device itself; however, use of multiple metering devices distributed throughout the building to achieve this type of feedback is both costly and cumbersome to install.
An alternative solution to the above is a single-point measurement system that non-intrusively detects which appliances and devices are turned on and off in a building. This concept has been worked on for years under several different names such as Non-Intrusive Load Monitoring (NILM), Non-Intrusive Appliance Load Monitoring (NIALM), Non-Intrusive Power Monitoring (NIPM), and Non-Intrusive Transient Classification (NITC). In general, previous non-intrusive load monitoring solutions commonly rely on two separate processing components: (1) a signal event detector to detect a change in the total electrical load that may have been caused by an appliance transition of interest; and (2) a classification algorithm that classifies features computed from the signal surrounding the event as belonging to a particular appliance transition based on a trained classification algorithm. One disadvantage of this approach is the need for the event detector which may lead to false positives or missed events and consequently inaccurate or missed classifications of the active appliances. Additionally, the degree to which the classification leverages the electrical load dynamics associated with a particular appliance depends solely on the features extracted from the signal and used in the classification. For instance, most of the features used in the machine learning algorithms center around changes in certain characteristics of the electrical load that occur around the event, such as changes in real power or the magnitude/phase of higher order harmonic contributions. The full range of dynamics is truly reflected only if an inordinate number of features are used. Furthermore, classification algorithms require several instances of a particular class or event to be trained into the system to be able achieve good results. This training process is also tedious and in many cases unpractical.
Additional approaches to nonintrusive load monitoring rely on continuously comparing scaled (in time and magnitude) versions of a template of a device event transient to the actual power signal being observed. Residuals computed from this comparison are used to estimate which appliance is responsible for the observed event. This approach is advantageous over the previously described event classification approaches in that a separate event detector need not be required (comparisons are made at all times), and the general shape of the transient is used in the comparison instead of just relying on a set of computed features to classify.
Non-intrusive load monitoring solutions have also been proposed that involve a combination of the above approaches. That is, a complete system may comprise an event detector followed by a combination of a steady-state change classifier (to identify which appliance or device most corresponds to the overall power consumption change) and a transient classifier (to select the best-matching signal transient template from a list of stored templates). These approaches may be implemented in parallel or series along with heuristics or optimal search algorithms so as to reduce computation time of searching over the space of all possible appliance events. Furthermore, other tools have been proposed to resolve potential errors that may accumulate with misclassification of events; these include finite state machine logic (to ensure, for example, that appliance A cannot turn on if it is already on) or a steady-state analysis tool that operates between events to refine the set of possible transitions that can occur with an event.
However, disadvantages of the above approaches still remain. First, they lack any direct use of models of the actual physical dynamics of the appliances in the estimation or classification process. Some work has been done on using physically-based dynamical system models in load diagnostics (e.g, fault detection); however, leveraging these dynamics for both state (of the appliance) estimation and the estimation of which appliances are turning on/off has not been done. Another disadvantage of prior art is the need for (local) batch processing of a power signal to perform the appliance classification instead of a truly iterative approach that is more conducive to implementation in a real, potentially embedded, system. Finally, previous approaches involve a separate treatment of events instead of a holistic view of a large system comprised of different operating modes or states that may have certain transition probabilities induced by user behavior or appliance operating modes (e.g., multi-state appliances). The invention described below proposes a solution that improves upon these disadvantages.
What is neither disclosed nor suggested in the art is a system and method for electrical load monitoring that overcomes the problems and limitations described above.