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Record W4391971830 · doi:10.2175/193864718825159316

Water Demand Forecasting Implementation: Best Practices for Improved Decision Making

2024· article· en· W4391971830 on OpenAlexaboutno aff
Jessica LeNoble, Colwyn Sunderland

Bibliographic record

VenueProceedings of the Water Environment Federation · 2024
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsDemand forecastingComputer scienceOperations researchEngineering

Abstract

fetched live from OpenAlex

Water Demand Forecasting Implementation: Best Practices for Improved Decision MakingAbstractNorth America's water and wastewater utilities have experienced many major changes in the past 20 years, but likely one of the largest has been the 'decoupling' of system demands from population growth. As populations in most communities have grown, water demands and dry weather sewer flows have decreased. This should be hailed as a major triumph of collaboration across North America between water utilities, regulators and fixture and appliance manufacturers, who have worked together to greatly improve the efficiency of water use in our homes and buildings. Unfortunately, the result has instead too often been hand-wringing about revenue shortfalls, concern over adverse impacts on drinking water quality or concerns for wastewater odor and corrosion. There has also been a reluctance to update engineering design standards, hydraulic models and utility master plans to reflect the new reality of lower per capita water use. Why do we seem to focus more on declining water demand as a problem than a major success story? How can we maximize the benefits and minimize challenges arising from changing water demands? In 2016, the Pacific Institute published a paper illustrating a large and consistent bias in water utility demand forecasts by large US water utilities over several decades and proposing a set of best practices to produce more reliable forecasts. In our experience, the historical tendency to over-predict future water demands is also common in Western Canada. Working with dozens of water and wastewater utilities, KWL has applied the Pacific Institute's advice to demand forecasting practices, enabling each community to tangibly benefit from their investments in water efficiency. The presentation will provide an overview of what we have learned and applied in our approach to water demand forecasting, using real-world examples from utilities serving 50 to 2 million customers that illustrate the benefits and applications of improved demand forecasting in utility management and decision-making. A good forecast begins with a good model of community water use. Key techniques that will be described include: 1)accounting for land use through sector and end use breakdown, 2)accounting for base and seasonal demand breakdown through land cover analysis, 3)estimating the impacts of climate change using climate models from the Pacific Climate Impacts Consortium, including the impacts of the 2021 Heat Dome event experienced by the Pacific Northwest, 4)evaluating population growth, economic uncertainty, and policy changes through scenario analysis, 5)incorporating non-revenue water in universally metered and unmetered systems, and 6)calibration and uncertainty assessment using Monte Carlo simulation. We will also focus on the issue of methodology implementation and methods for risk management when onboarding planning and engineering staff to transition from a previous, possibly overly simplified, forecasting methodology to a revised forecasting methodology, which applies best practices. Four real-world examples from Western Canada will be case studied to show how these forecasting techniques have enabled utilities of all sizes to: avoid or defer capital and operating costs of water supply and wastewater treatment, target specific sectors and end uses of water or wastewater with cost-effective demand management measures, establish effective seasonal watering restrictions that address water supply risks, set utility rates that encourage conservation while maintaining stable revenues; and establish design standards for efficiently sized future infrastructure, and evaluate system wide cost savings and benefits for the utility. Case Study 1: a mid-sized utility uses their demand forecast to support an evaluation of the risks for revenues with recent changes in population growth projections. Case Study 2: a large BC utility incorporates scenario analysis into their forecast to evaluate the implications for forecasting on timing for both a new water supply project and a chemically enhanced wastewater process system. Case Study 3: a large Alberta utility evaluates the benefits achieved by their water conservation program versus natural fixture replacement, alone. Case Study 4: the benefits of customer metering are evaluated by comparing demand forecasts for two mid-sized BC utilities one with universal metering and one without.This paper was presented at the WEF/AWWA Utility Management Conference, February 13-16, 2024.SpeakerLeNoble, JessicaPresentation time11:00:0011:30:00Session time10:30:0012:00:00SessionUtility Planning: Essential to SuccessSession number19Session locationOregon Convention Center, Portland, OregonTopicStrategic Planning and ImplementationTopicStrategic Planning and ImplementationAuthor(s)LeNoble, JessicaAuthor(s)J. LeNoble1, C. SunderlandAuthor affiliation(s)Kerr Wood Leidal Associates Ltd 1;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Feb 2024DOI10.2175/193864718825159316Volume / Issue Content sourceUtility Management ConferenceWord count11

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.248
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
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