This paper discusses how Artificial Neural Network (ANN) technology served
as a valuable tool in making conservation program choices in an ongoing residential water
conservation program for Edmonton, Alberta, Canada. The focus of this study was to
identify significant influencing factors for residential water consumption and use this
information to adopt public education programs that would best influence the behavior of
residential water users. Artificial Intelligence (AI) was used in-house to create an ANN model that forecasts residential water
consumption levels. One of the most important steps in creating the model was determining
the statistically significant factors that have affected residential water consumption in
Edmonton over the last 30 years. Now complete, the model can be used to predict
consumption forecasts based on various combinations of these factors. The ANN model
provided the list of the main factors that influence water consumption as well as a means for
running multiple scenarios based on a range of values for the various factors. Each
scenario produced a consumption prediction. The degree of variability of these
consumption predictions was analyzed to determine which factors have the greatest impact on consumption. The order of significance of impact of these factors on residential
consumption proved to be:
base consumption index (a measure of the lowest year-round minimum usage);
weather (summer and winter months); and,
customer count (can be most or least significant, depending on the span of years
studied). Determining the
order of significance of the main residential consumption drivers helped identify which
factors could be targeted through public water conservation initiatives. ANN technology provided the information necessary to eliminate options and to focus
resources on the most effective and cost efficient programs for the community. In order to create a targeted residential water conservation program, the study researched
these 5 questions:
is the ANN model predicting consumption with a reasonable degree of accuracy;
what is the range of possible residential water consumption (these values were used
as a check on later calculations);
what factors drive residential water consumption (these factors were determined
during development of an ANN residential water consumption forecasting model);
how much can the changes in each consumption driver affect total residential water
consumption (the degree of variability was assessed by running scenarios with the
ANN model); and,
which factors can be influenced by water conservation campaigns?
Includes 6 references, figures.
| Edition : | Vol. - No. |
| File Size : | 1
file
, 310 KB |
| Note : | This product is unavailable in Ukraine, Russia, Belarus |
| Number of Pages : | 13 |
| Published : | 06/17/2004 |
