Database for Water Supply Systems Aiming at Hydro Energy Efficiency Using R and EPANET

Recommendation: Design and deploy a centralized relational database to store water-supply network data, EPANET input files, hydraulic and energy performance metrics, and R-based analysis results. The schema should be modular, with separate domains for hydraulic topology, energy indicators, and water quality data, enabling specific queries and repeatable procedures that can be reused across sites. This approach, therefore, optimizes data integrity and speeds up simulation-driven decision making without duplicating records.

In practice, model design should be designed to capture network topology, pump curves, reservoir levels, energy costs, and flow patterns. Include tables for nodes, links, tanks, pumps, energy tariffs, pollutants, and tracer results. Use EPANET rules to map hydraulic states to time-stamped observations. The application uses R for data cleaning, statistical analysis, and simulation orchestration, and stores results as non-redundant fetches from EPANET runs. This supports robust sensitivity analysis and scenario comparison, therefore enabling rapid policy testing.

Quality and safety data: incorporate water-quality parameters (chlorine residuals, turbidity, pollutants), tracer tests, and contamination events. The keeney guidelines and the hunaidi references guide best practices for modeling pollutants and chemical reactions. Your procedures should document procedures for data validation, calibration against field measurements, without compromising data provenance. The database should store obtidos from field sensors and lab analyses, with metadata describing measurement accuracy and sensor placement.

Data ingestion and application: set up an intake procedure to ingest live sensor data, EPANET export, and energy tariffs. Use a designed pipeline to trigger R-based simulations when key triggers occur (demand spikes, pump failures, or pollutant excursions). This application helps operators maintain safe operations, identify causes of efficiency loss, and prevent pollutant intrusion, therefore improving hydro-energy performance.

Governance and interoperability: adopt standard data formats, the tracer approach for source tracing, and clear procedures for data backups. The database architecture should 지원 scalability to handle growing networks and multiple sites, with role-based access controls and regular practices for data sharing between utilities, researchers, and operators. keeney 그리고 hunaidi guidelines reinforce compatibility with cross-utility standards, ensuring data quality across projects.

Database for Water Supply Systems Aiming at Hydro Power Optimization Using R and EPANET; Preparing for Future Water Scarcity

Start by implementing a centralized database that links measured water quantities to hydro power potential and seamlessly coordinates EPANET model runs with R workflows. This setup accelerates daily decisions and supports projects across geographic regions, including basins with variable rainfall and storage dynamics.

In the database, define attributes such as station_id, geographic coordinates, inflow and outflow (m3/s), head, reservoir storage, turbine efficiency, energy output, and measured values. Use regular updates for values and include sources and data provenance. This structure supports scalable processing and cross-project comparisons.

Link DERMS data to hydraulic constraints to reveal how electrical grid controls affect water operation; store equipment status and outages alongside hydraulic states so models capture coupled water and power risks. Moreover, this integration helps identify issues early and promotes optimization of pump duty cycles and turbine selection, with clear sense of how changes propagate through the system.

Processing pipeline ingests data from SCADA, sensors, and manual logs; performs quality checks; flags anomalies; computes derived attributes and total energy potential; and stores daily aggregates. Document data provenance and enforce checks to minimize errors; this processing sometimes reveals issues that affect subsequent analyses.

Geographic segmentation enables region-specific optimization: create sub-databases by basin, sub-basin, and climate zone; apply variable hydrology and seasonal patterns to stress tests. This geographic granularity improves sense-making for operators and planners and reduces data latency.

Literature-informed practices guide data models and products for monitoring and modeling. Include references from literature and ensure compatibility with a range of products while aligning with standards. Moreover, maintain a living glossary to harmonize terms across sources, enhancing the total value of the dataset.

Preparing for future water scarcity requires scenario planning and alternative sources; integrate desalination, reclaimed water, rainwater harvesting, and groundwater exchanges as sources in the same framework. The database should run scenarios to identify the least costly options and promoting resilient operation while reducing outages.

Programa modules expose a friendly API for analysts to fetch attributes, values, and processed metrics, while ever-crucial access controls enforce least-privilege data sharing. This structure supports daily collaboration without compromising security or data integrity.

Daily dashboards summarize total energy potential and geographic distributions, while sense-making visualizations support decision-makers. Name drought scenarios Macbeth to facilitate cross-project comparisons and maintain a clear audit trail, translating metrics into daily words for operators.

Promoting a data-centric culture requires regular validation, clear documentation, and seamless integration with existing enterprise tools. The result is an adaptable database that optimizes hydro power while safeguarding water supply under scarcity.

Data Architecture and Practical Workflows for R-EPANET Integration

Start with a centralized, versioned data store that holds network topology, material properties, dmas, and ders, and set automated pipelines that push flows, demands, and sensor readings into R-EPANET models. This approach keeps data consistent, accelerates scenario testing, and makes results traceable as the number of simulations grows.

Represent the network as graphs with nodes (junctions, tanks, reservoirs) and links (pipes, pumps, valves), attaching static attributes and time-series for flows and demands. Align units and coordinate references, and tag regulatory attributes to support compliant analyses and straightforward audits of derived metrics such as head loss and energy use in operations.

Sourcing data from SCADA, AMI, GIS, and operator logs should feed a clean ETL layer that translates to EPANET-ready fields, with explicit versioning and timestamps. Store copies of raw, cleaned, and validated data to enable back-testing and reproducibility across increasing numbers of runs, while preserving data lineage for audits and practice reviews.

Define practical workflows: nightly ingestions update network parameters, R-EPANET runs execute hydraulic and, where applicable, water quality simulations, and results land in a dedicated results table keyed by run_id, timestamp, and scenario. Use modular steps to separate data preparation, model parameterization, simulation, and reporting for easier maintenance and faster iterations.

Adopt governance that ties data quality to dmas and ders classifications. Hutton proposes a modular taxonomy for materials, sources, and sensing assets, while Vernovas offers a catalog of instrument types and sensor provenance. Apply QC checks at ingestion and after each run to catch unit mismatches, missing values, and outliers before they skew decisions or regulatory submissions.

Provide clear sense-making outputs: graphs and tables that summarize reliability, peak flows, and pump energy across scenarios. Store summary metrics alongside detailed time-series results, enabling operators and businesses to compare operations under varying demand patterns and regulatory constraints while maintaining audit-friendly traceability.

Plan for sourcing and optimization of hydro energy efficiency by modeling how flow adjustments and pump schedules affect energy use. Include the most impactful DERs in dmas contexts, and use the stored history of runs to identify robust operating envelopes. Keep practice notes and data dictionaries current, and use Verged naming conventions to simplify collaboration among teams and suppliers.

Design EPANET-ready schemas for pipe networks and reservoirs in SQL or CSV

Design EPANET-ready schemas by modeling pipes and reservoirs as distinct tables with stable IDs and clear relationships; this approach minimizes data losses and supports reliable monitoring across workflows. The general design follows EPANET’s data model and proposes a modular schema that integrates nodes, edges, tanks, and reservoirs into these components. It remains platform-agnostic and works with SQL databases or CSV exports, offering consistent data ingestion into EPANET and R for hydraulic analysis.

Core tables and key fields ensure compatibility with EPANET elements and provide robust characteristics for energy efficiency studies. Nodes store node_id, name, type (Junction, Reservoir, Tank), elevation, x_coord, and y_coord. Pipes capture pipe_id, from_node, to_node, length_m, diameter_mm, roughness, and status. Tanks map tank_id to node_id with diameter_m, height_m, initial_level_m, min_level_m, and max_level_m. Reservoirs attach reservoir_id to node_id with head_m, min_head_m, and max_head_m. Pumps define pump_id, from_node, to_node, curve_id, speed_rpm, and status. Valves hold valve_id, from_node, to_node, type, and setting. Demands link node_id with pattern_id and base_demand_LPS, while Patterns cover pattern_id, time_step, and multiplier. PipeHeadLoss or equivalent parameters may be stored per pipe to capture friction factors and headloss characteristics, enabling better alignment with hydraulic calculations. These options support a consistent combination of network geometry and hydraulic parameters across SQL or CSV sources.

Data integrity and relationships follow best practices: enforce foreign keys from Pipes to Nodes, Pumps to Nodes, and Demands to Patterns; require non-negative values for length_m, diameter_mm, height_m, and head values; use unit mappings to ensure diameters, lengths, and flows stay consistent when exporting to CSV. These constraints raise reliability and meet general requirements for reproducible simulations. Indexes on node_id, pipe_id, and pattern_id accelerate queries that assemble network topology and time-varying demands.

CSV export guidelines keep interfaces simple for R and EPANET imports. Use clearly named headers that mirror field labels (node_id, pipe_id, from_node, to_node, length_m, diameter_mm, roughness, tank_id, head_m, pattern_id, base_demand_LPS). Store units in a separate metadata file and include a version tag for schema evolution. Provide sample rows for a small test network to validate mapping between SQL data types and CSV text formats, ensuring both platforms read the same characteristics and maintain consistent values across pipelines and reservoirs.

Link EPANET hydraulic results to R data frames for quick plotting

Export EPANET results to CSV after each simulation and load them into two tidy R data frames for quick plotting: one for nodes (geographic positions, demand, head) and one for links (flow, velocity, status). This approach supports measuring hydraulic behavior across times, thus helping to compare decentralized configurations and assess scarcity risks under varying demand patterns.

• Define a consistent schema: nodes(id, x, y, type, demand, head, pressure) and links(id, from, to, length, diameter, flow, velocity, status). Include a time column in both frames to enable time-based plots and multi-criteria comparisons.
• Load and validate data in R: results_nodes <- read.csv("epanet_nodes.csv"); results_links <- read.csv("epanet_links.csv"); check types and units, then convert time to POSIXct using as.POSIXct(times, format="%Y-%m-%d %H:%M:%S").
• Merge with geographic data: if you have geographic coordinates, join results_nodes with a spatial dataframe to enable plotting on a map. Use sf or sp objects and coord_sf for accurate geographic graphs.
• Create quick time-series graphs: plot head or pressure over time for selected nodes, and plot flow or velocity over time for key links. Use ggplot2 with facet_wrap for comparing multiple nodes or links in a single figure.
• Compare demand scenarios: compute daily or hourly summaries (mean, max, percentile) and visualize how changes in demand affect pressure and flow. This supports measuring whether targets are met and identifying bottlenecks in least-cost configurations.
• Multi-criteria scoring: define a simple score combining reliability (pressure above threshold), service level (demand satisfaction), and energy implications (flow regimes). Compute within R and visualize heatmaps or radar plots to reveal shifts across scenarios.
• Procedures for reproducibility: store a para file with file paths, thresholds, and weights; script the import, cleaning, and plotting steps so analyses can be replicated across times and simulations (simulada). Keep a log of runs to monitor evolving goals and improvements.
• Quantify impacts with concise metrics: average head deficit, percent of nodes below target pressure, total flow deviations, and total simulated energy consumption. Present results in graphs and concise tables to guide decisions on demand management and energy efficiency.
• Practical tip: to speed plotting, pre-aggregate by node or link at each time step and then render only the summarized series; this reduces rendering time when working with large networks and numerous time steps.

Example workflow in R (conceptual):

results_nodes <- read.csv("epanet_nodes.csv"); results_links <- read.csv("epanet_links.csv"); results_nodes$time <- as.POSIXct(results_nodes$time); results_links$time <- as.POSIXct(results_links$time); library(dplyr); summary <- results_nodes %>% group_by(node_id, time) %>% summarize(mean_head = mean(head), max_head = max(head));

Create reproducible pipelines with R scripts to run EPANET scenarios

Adopt a Git-driven, project-wide reproducible pipeline in R to run EPANET scenarios across locations. Store core components: EPANET INP files, parameterized scenario definitions, and R scripts that produce clean results on a dedicated server. This setup enables colleagues to reproduce results, add new sites, and audit conservation gains.

Structure the workflow into a core sequence: data preparation, simulation, and results reporting. Use a wrapper function run_scenario(scenario, inp) that returns a tidy data frame with location, demand multiplier, energy use, and head pressure; run scenarios in parallel to speed up and keep the process seamless across cores. Focus on a lightweight data model that ties inputs to outputs, so adding a new site or scenario remains straightforward.

Define addition of scenario templates: specify demand shifts at fixed locations, adjust pump curves, and tune valve openings; maintain a global scenario catalog to enable easier comparison; utilize imputation for missing demand data to avoid gaps. Store the scenario metadata in a single reference table to support consistent across-site comparisons and auditability.

Leverage infrastructure: a server or cloud instance with multi-core support; use R packages like future and furrr to map over sites and scenarios; capture results in a centralized table so results can be queried by location or scenario; ensure logs and error handling are in place to support serious debugging and traceability.

Criterion for acceptance: keep all sites above a minimum pressure, e.g., 25 m, while targeting energy reductions of 10-25% depending on the location; compute a composite score balancing conservation and reliability; escalate any scenario that loses service at a site to the review stage for refinement.

Results from the pipeline play a decisive role in informing decisions about infrastructure investments and policy measures. The reproducible setup makes it easier to compare outcomes across sites, support addition of new locations, and demonstrate the value of targeted changes in a transparent, auditable way.

Compute water age, energy consumption, and head loss metrics from simulations

Export the EPANET results to a structured data frame and compute three metrics per location: water age, energy consumption, and head loss, using R and EPANET. This approach supports storage monitoring and enables decisions for energy-efficient operations without outage.

Compute water age by tracking the time water spends from source entry to each node. Retrieve node age from EPANET, aggregate by location and storage tank, and plot histograms to reveal patterns of stagnation. Report 5th, 50th, and 95th percentiles, and compare weekday versus weekend schedules. These measures help you sense where stagnation occurs and where flushing or reservoir turnover is needed. Those patterns guide targeted operations and keep within safe ranges.

To quantify energy consumption, compute pump power as P = Q × H × η, with η in the typical range 0.6–0.8. Derive energy over a period E = P × Δt, summing across all pumps. Normalize by pumped volume to obtain energy per cubic meter. Track patterns by location and time‑of‑day to identify bottlenecks and opportunities for optimization; reporting per day and per pump clarifies where to upgrade pumps or adjust controls.

Compute head loss metrics: extract pipe head losses from hydraulic results, aggregate to system-wide and per-km levels, and report total head loss, mean loss, and maximum loss per corridor. Use a chosen model (Darcy–Weisbach or Hazen–Williams) and store the results with a timestamp. Mapping these values by location highlights critical links and informs maintenance to reduce outage risk.

Integrates these measures into a decision-support workflow that aligns with standards. The paper demonstrates how to monitor and treat data from EPANET, promoting optimization across patterns and storage locations. The approach supports almeida’s findings on localized network response and helps decision makers promote energy efficiency and reliability. This stance aligns with almeida.

Practical tips: keep results within a consistent schema, store as CSV or Parquet, and ensure reproducibility. Compute daily aggregates, validate inputs, and set up automated checks to ensure energy and age values stay within physical limits. Use clear naming for location, component type (node or pipe), and timestamp to enable rapid filtering and trend analysis.