AWWA JAW56653 Journal AWWA - Short-Term Water Demand Forecast Modeling Techniques - Conventional Methods Versus AI

AWWA期刊-短期需水量预测建模技术-常规方法与人工智能

基本信息

标准号

AWWA JAW56653

标准状态

现行

发布单位或类别

美国-美国给水工程协会(US-AWWA);

发布日期

实施日期

废止日期

CCS分类

ICS分类

研制信息

起草单位

- 展开▼

起草人

归口单位

摘要

A water utility's primary purpose is to provide safe drinkingwater to its customers, but it must also plan for its customers'future water needs. A critical aspect of this planningis predicting short-term (peak) water demands and optimizingthe water supply system to meet these demands. Jain and Ormsbeeevaluated eight models (four conventional and four artificialintelligence [AI]) to forecast short-term water demand. The modelswere developed and tested using daily water demand, dailymaximum air temperature, and daily total rainfall.AI models outperformed the conventional models and predictedshort-term water demands during both "normal" and drought conditionsrelatively accurately. These AI models can be run using commercialsoftware, if the system operator is experienced in usingmodels, or simple rule-based AI models can be easily developedand applied using historical demand, weather data, and a standardspreadsheet program. The authors encourage utility operators ormanagers to incorporate a short-term water-demand forecastingmodel into their predictions of future water needs. Includes 25 references, tables, figures. 自来水公司的主要目的是提供安全饮用水向客户供水，但它还必须为客户的需求制定计划未来的水需求。这项计划的一个关键方面预测短期（峰值）需水量并优化供水系统必须满足这些需求。杰恩和奥姆斯比评估了八个模型（四个常规模型和四个人工模型）用于预测短期需水量的智能[AI]）。模型使用每日需水量、每日最高气温和每日总降雨量。人工智能模型的表现优于传统模型，并预测“正常”和干旱条件下的短期需水量相对准确。这些人工智能模型可以使用商业软件运行软件，如果系统操作员有使用经验模型，或简单的基于规则的人工智能模型可以很容易地开发并使用历史需求、天气数据和标准电子表格程序。作者鼓励公用事业运营商或管理者需要将短期水需求预测纳入其中模拟他们对未来用水需求的预测。包括25个参考文献、表格和图表。展开▼

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