能源管理系统能源管理功能介绍（修订版）.ppt

Advanced algorithms accurately model energy performance based on historical characteristics and all relevant drivers. Enables more accurate benchmarking and comparison of facilities or processes against one another, baselining of performance and validation of actual savings, and forecasting of energy needs. Regression and correlation based on ASHRAE Guideline14, Measurement of Energy and Demand Savings. Normalises energy consumption data by removing multiple independent variables such as: weather conditions across different times or locations, square footage of different facilities, or production volume for different plants. Provides context by integrating external data on equipment, building or other assets such as: load/performance/efficiency ratings, age, total/leasable space, occupancy rates, and more. Allows variables to be changed to gauge dependencies and outcomes. * happy every day Accurately allocates energy costs by cost centre, department, production line or user-defined time periods, based on actual energy usage. Allocates and breaks out charges for each utility type (e.g. electricity, steam, gas, others) and supports virtual metering (values derived from other meters) Simplifies the cost allocation process and ensures total utility costs are allocated by using a blended energy rate (total utility bill cost divided by the total energy usage) that captures all energy charges, demand charges, fees, and taxes in a single energy rate. Applies adjustment factors to any energy values that have not been metered: In cases where the sum of the sub-meters does not equal the energy recorded by the main meter. Compensates for system losses. Integrates charges with other analysis tools and dashboards, including making cost allocation results available to the Trend Analysis module for further evaluation. Distributes cost allocation reports via e-mail or web. * happy every day System-wide power quality and reliability analysis helps quickly identify and isolate