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Enregistrement W4391971830 · doi:10.2175/193864718825159316

Water Demand Forecasting Implementation: Best Practices for Improved Decision Making

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

Notice bibliographique

RevueProceedings of the Water Environment Federation · 2024
Typearticle
Langueen
DomaineEngineering
ThématiqueWater resources management and optimization
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDemand forecastingComputer scienceOperations researchEngineering

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,368
Score d'incertitude au seuil0,424

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,248
Écart entre enseignants0,224 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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Même revueProceedings of the Water Environment FederationMême sujetWater resources management and optimizationTravaux en français237 207