A Systematic Literature Review on Machine Learning Techniques for Predicting Household Water Consumption

Zenodo Repository Metadata and Description

Title: Supplemental Material for "A Systematic Literature Review on Machine Learning Techniques for Predicting Household Water Consumption"

Description

This repository contains the dataset and methodological artifacts developed for a Systematic Literature Review (SLR) investigating Machine Learning (ML) applications in residential water demand forecasting. The review synthesizes evidence from 80 primary studies published between 2009 and 2024, following the PRISMA-2020 guidelines.

Repository Contents

DataExtraction.csv: The master evidence matrix containing metadata, objectives, and findings for all 80 primary studies.

Taxonomy Maps: Standardized mapping files that document how raw textual terms from the literature were normalized into canonical categories.

Supplementary List of Papers: A unique identifier (ID) list (P01–P80) with full bibliographic references.

Key Research Insights

Methodological Evolution: A capability shift around 2020 where Deep Learning models (notably LSTM) displaced traditional methods.

Predictor Patterns: Climatic and historical consumption dominate, with limited use of socio-economic variables due to privacy constraints.

Evaluation Standards: RMSE and MAE are the most widely used evaluation metrics.

Practical Tools for Researchers

Model-Predictor Crosswalk: A co-occurrence matrix of model families and predictor categories.

Prescriptive Decision Tree: A heuristic framework guiding model selection based on data and computational constraints.