Comparison of peaking factor of measured flow data from commercial users with empirical formulas

Population growth and economic development are major drivers of increased global freshwater demand, with urban populations expected to rise from $54 \%$ to $66 \%$ by the century’s end. The Organization for Economic Cooperation and Development (OECD) predicts a $55 \%$ increase in water demand since 2000, potentially putting over $\mathbf{4 0 \%}$ of the global population under severe water stress by 2050. Climate change and urbanization will exacerbate the challenge of ensuring urban water security. While expanding water infrastructure could help, geographical and financial constraints limit this approach. Alternatively, managing water demand through water-saving technologies, updated policies, and education can ensure a reliable supply and reduce costs. Residential water consumption in Panama has significant societal impact but is inefficiently tracked with traditional manual meter readings. Smart water meters provide a modern solution, offering continuous electronic readings. Despite the high initial costs, smart water meters improve water registration accuracy and management efficiency, enabling real-time usage monitoring and generating valuable datasets for water resource management. This study analyzed continuous water consumption data from a smart meter network at Universidad Tecnológica de Panamá’s cafeterias to calculate peaking factors and compare them with empirical formulas. Using a LoRaWAN framework, water flow data was collected, stored, and analyzed, with missing values imputed using Python. Significant discrepancies were found between peaking factors from empirical formulas and actual data, highlighting the need for extensive flow metering operations. The recorded water demand pattern matched the building’s daily activity, emphasizing the importance of smart meters for accurate water consumption estimation and improved management.