Large buildings often incorporate computerized control systems which manage the operation of different subsystems, such as for heating, ventilation and air conditioning. In addition to ensuring that the subsystems perform as desired, the control system operates the associated equipment as efficiently as possible.
A large entity may have numerous buildings under common management, such as on a university campus or a chain of stores located in different cities. To accomplish this, the controllers in each building gather data regarding performance of the building subsystems so that the data can be analyzed at the central monitoring location.
With the cost of energy increasing, facility owners are looking for ways to manage and conserve utility consumption. In addition, the cost of electricity for large consumers may be based on the peak use during a billing period. Thus, high consumption of electricity during a single day or billing increment can affect the rate at which the service is billed during an entire month or longer. Moreover, certain preferential rate plans require a customer to reduce consumption upon the request of the utility company, such as on days of large service demand throughout the entire utility distribution system. Failure to comply with the request usually results in stiff monetary penalties which raises the energy cost significantly above that for an unrestricted rate plan. Therefore, a consumer must have the ability to analyze energy usage data to determine the best rate plan and implement processes to ensure that operation of the facility does not inappropriately cause an increase in utility costs.
The ability to analyze energy consumption or usage data is particularly important for consumers that subscribe to real-time pricing (RTP) structures. With RTP structures, utility companies can adjust energy rates based on actual time-varying marginal costs, thereby providing an accurate and timely stimulus for encouraging customers to lower demand when marginal costs are high. To benefit from RTP, the consumer must have the ability to make short-term adjustments to curtail energy demand in response to periods with higher energy prices. One increasingly popular method of accomplishing this objective is by supplementing environmental conditioning systems with energy storage mediums, such as ice-storage systems. To maximize the benefits from such energy storage mediums, the consumer must have not only the ability to analyze energy demand and consumption information but also the ability to predict future load requirements.
The ability to analyze energy or utility consumption is also of critical importance in identifying abnormal consumption. Abnormal energy or utility consumption may indicate malfunctioning equipment or other problems in the building. Therefore, monitoring utility usage and detecting abnormal consumption levels can indicate when maintenance or replacement of the machinery is required.
As a consequence, sensors are being incorporated into building, management systems to measure utility usage for the entire building, as well as specific subsystems such as heating, ventilation and air conditioning equipment. These management systems collect and store massive quantities of utility consumption data which can be analyzed by the facility operator in an effort to predict future load requirements and/or detect anomalies.
In practice, it is impossible to accept data, uncritically, 24 hours a day, 365 days a year. For example, sensors and meters can fail, as can the infrastructure used to transmit the data from its point of origin to where it is logged and analyzed. Thus, whenever energy or utility consumption data is recorded there is a good chance some of it will either be lost or rejected as not valid. While some types; of summary calculations (e.g., averages) are relatively “immune” to gaps in the data, other calculations (e.g., aggregations) require a complete data set for reasonable accuracy. Thus, a utility bill cannot be calculated unless all of the consumption and demand data (either actual or estimated) is available for the billing period because of the aggregations that are required. Another problem that is unique to calculating a utility bill is that if the missing data corresponds to a period of peak demand, the amount of the utility bill can be significantly impacted.
There are numerous strategies known in the utility industry for estimating or gap filling missing consumption data. These strategies range in complexity from simple linear interpolation to higher-order curve fitting techniques based on advanced mathematics such as linear and nonlinear least squares, recursive and sequential least squares, minimum mean square error, and the like. A systematic and comprehensive treatment of rigorous methods for analyzing data with missing values is provided in Statistical Analysis With Missing Data, 2nd Edition, Little, R. J. A. and Rubin, D. B., New York: John Wiley (2002). However, most of these approaches work well only for relatively narrow gaps in the data. Other known problems with these techniques are they can require extensive learning periods and/or tend to be relatively poor at replicating the periodic behaivor of many signals.
In the utility industry it is also common to use standard load or usage profiles for estimating a customer's energy usage. These standard usage profiles are typically based upon the utility usage of a customer class and divided into weekday, weekend day, and holiday types. With some of these approaches, the usage profile may be adjusted for the temperature and weather observed in the recent past. Although this works well in some instances, it does not work well for customers having non-standard utility usage such as a manufacturing plant that is on four days and off three days each week. As a result, these customers may be assessed utility charges (e.g., “demand” or “consumption”) that can be significantly more or less than is justified based on actual utility usage.
Accordingly, it would be advantageous to provide an improved method and system for estimating missing data over large gaps in systems having periodic behavior so that accurate summaries and other calculations can be performed. It would also be desirable to provide a method and system for computing individual usage or consumption profiles for each customer using data obtained from the sources actually being profiled so that the customer classification usage based profiles can be avoided.