This powerpoint presentation focuses on
short-term consumption forecasting (STCF)
techniques for energy management
(AwwaRF Project #3066
Water Consumption Forecasting to
Improve Energy Efficiency of Pumping Operations). The first section of the presentation outlines short-term
consumption
forecasting -
a key strategy to
minimize
increasing energy
costs and includes these topics: operating costs for 19 sampled water
utilities in the 2003 AwwaRF Project; Electric Costs Rising Dramatically Due to
Increasing Fuel Costs; Other Factors Pushing Up Electric
Energy Cost; Water Utility Responses to Rising Costs; Short Term Consumption Forecasting (STCF)
Initiates Optimized Operational Pump
Scheduling Through an Energy & Water
Quality Management System (EWQMS); Forecasting Consumption Moves Water
Utility from Reactive to Proactive
Operations Using an EWQMS; Pumping Operations are Typically
Consumption-Following; STCF's Objective: Predict Hourly Consumption
for Multiple Areas in the System and Move Water
from Source to Customer at Lowest Cost; STCF Enables Time Value Purchasing
and Selling Energy - A Supply and
Demand Strategy; STCF Window (Daily and Hourly)
Depends on Application; Time-of-Use Pumping Strategy
Based on a Forecast; Example of Time-of-Use, Demand, and
Efficiency Strategies; STCF Enables Significant
Water Supply Opportunities; and, Scheduling Treatment Plant and Import Water
Production Schedules Based on Short Term
Forecasting has Saved San Diego $Millions. The second part of the presentation provides a description and the
results of the AwwaRF
Project #3066,
"Water Consumption
Forecasting to
Improve Energy
Efficiency of
Pumping Operations" and provides an overview of the project and participating facilities. The four steps of the forecasting project include: Research Existing Forecasting
Techniques Used at Water,
Gas, and Electric Utilities; Analyze Operational Results
at Four Water Utilities Using
STCFs; Develop and Test Prototype
STCFs Under Operating
Conditions at Five Water Utilities; and, Analyze Data and
Publish Results. The third part of the presentation provides conclusions and lessons learned:
accuracy is highly dependent on quality of
historical data;
other than temperature and rainfall, weather
may not be as important as one would think;
retraining an ANN may degrade the
model;
ANN models hold up well over time;
Training ANN models with a full year of data
provides better results than training with only
a single season of data; daylight savings is problematic for all
forecast models;
large number of input parameters can make
models less responsive and more difficult to
maintain;
Forecast is limited by accuracy of
measurement equipment; and,
a short-term consumption forecaster "enables"
proactive system operations to save
substantial energy and other operating costs.
| Edition : | Vol. - No. |
| File Size : | 1
file
, 11 MB |
| Note : | This product is unavailable in Ukraine, Russia, Belarus |
| Number of Pages : | 38 |
| Published : | 06/01/2007 |
