Aggregate-Load-Analysis

HPWH Monte Carlo + OCHRE Parsing + Visualization Toolkit

A collection of Python scripts used to parse OCHRE heat pump water heater data, run Monte Carlo sampling, compute confidence intervals, and generate plots + Word documents.

Authors: Jeff Dinsmore, Joe_admin, ChatGPT

Table of Contents

• Get_marginal_load_final.py
• P95percent_confidence_data_by_time.py
• parse_OCHRE_data_final.py
• Plot_3D_surfaces_final.py
• plot_aggregate_load.py
• png_to_word.py

Description

This folder contains several standalone Python scripts used in an HPWH (heat pump water heater) workflow:

• parse_OCHRE_data_final.py converts raw OCHRE exports into a “ready” pivoted dataset (Homes × 15-minute time columns).
• Get_marginal_load_final.py runs Monte Carlo sampling for increasing fleet size (1…N units), aggregates sampled loads, and outputs time-series statistics (mean, quantiles, variance, etc.) for each fleet size.
• P95percent_confidence_data_by_time.py reads the Monte Carlo output files and, for each 15-minute time step, visualizes the mean and percentile bands vs number of units; it also writes a compact per-time summary CSV (P_mean_*) containing mean/95th/5th values.
• Plot_3D_surfaces_final.py creates 3D surfaces for the 97.5th percentile, mean, and 2.5th percentile across (time, units), including highlighted slices.
• plot_aggregate_load.py compares baseline vs controlled aggregate profiles for a chosen fleet size, computes energy metrics (kWh), and generates multiple plots (baseline, controlled, and differences).
• png_to_word.py interleaves baseline/control PNG figures into a formatted Word document grid for reporting.

Get_marginal_load_final.py

Purpose: Monte Carlo sampling of HPWH load profiles for fleet sizes 1…unit_runs, computing time-series statistics per fleet size. Inputs: A “ready” CSV with a Home column and 15-minute time columns (e.g., 00:00, 00:15, …). Outputs: CSV files for: 0.975 quantile, mean, 0.025 quantile variance, standard deviation, skew Notes: Uses a scaling to per-unit / per-fleet normalization (MCS_table.div(0.5 * N)), where 0.5 is a domain-specific scaling constant (HPWH case).

P95percent_confidence_data_by_time.py

Purpose: For each 15-minute time step, plot mean + percentile bands vs number of units, compute a linear fit, and export a compact CSV summary. Inputs: The Monte Carlo stats CSVs (97.5th, mean, 2.5th) and a ready CSV to determine the number of homes/rows available. Outputs: Appends rows to P_mean_control_1000.csv (or similar) with columns: time, P_mean_kW, 95th, 5th Notes: Produces per-time plots and a “CI width / mean (%)” curve on a secondary y-axis.

parse_OCHRE_data_final.py

Purpose: Convert raw OCHRE export data into a clean pivot table format for downstream Monte Carlo sampling. Inputs: Raw OCHRE CSV containing a Time column, Home, and Water Heating Electric Power (kW) (plus other columns). Outputs: A pivoted “ready” CSV where: rows = Home columns = hr_min in HH:MM format values = Water Heating Electric Power (kW) Notes: Creates the output folder if it doesn’t exist.

Plot_3D_surfaces_final.py

Purpose: Visualize 3D surfaces for percentile/mean load vs time and vs number of units. Inputs: Three CSVs (97.5th, mean, 2.5th) produced by the Monte Carlo pipeline. Outputs: Interactive 3D plot window (matplotlib). Notes: Includes example highlights for a specific N and a specific time slice.

plot_aggregate_load.py

Purpose: Compare baseline vs controlled HPWH aggregate load profiles for a selected fleet size and compute energy metrics. Inputs: P_mean_baseline_.csv and P_mean_control_.csv (generated by the confidence-by-time workflow). Outputs: Multiple plots: Controlled − Baseline Baseline − Controlled Overlay of Baseline vs Controlled Per-segment 15-minute energy bar plot Optional exported CSV for per-interval energy (e.g., segment_energy_baseline.csv) Notes: Includes helper functions for kWh computation and dynamic engineering unit scaling (kW/MW/GW).

png_to_word.py

Purpose: Build a Word doc that interleaves baseline/control PNG plots in a grid layout, page by page. Inputs: Two folders of PNGs (./baseline_images, ./control_images by default). Outputs: A Word document (default: baseline_control_interleaved.docx). Notes: Uses python-docx, sets 0.5" margins, and paginates after each filled grid.

Setup/Installation Requirements

• Install Python WSL or Ubuntu
• Install Python 3.10+ (recommended)
• Verify installation: python3 --version (WSL) or python --version (Windows)
• Clone this repository
• Open repository in your preferred code builder program. (This was built in VS Code)_
• In the terminal, cd into the Aggregate_Load_Analysis folder Optional:
• Create a virtual environment and install packages
• Create venv: python3 -m venv .venv

Activate:

Install dependencies (WSL/Ubuntu using sudo + apt):

• sudo apt update
• sudo apt install -y python3-pandas python3-numpy python3-matplotlib python3-sklearn python3-docx

Known Bugs

No known bugs at the this time.

Support and Contact Details Any known issues, please reach out to Jeff Dinsmore by email at hello@jeffdinsmore.com

Technologies Used

• Windows 11
• WSL/Ubuntu
• Python 3
• Pandas
• NumPy
• Matplotlib
• scikit-learn
• python-docx
• VS Code

License

Copyright (c) 2025 - Updated Feb 2026, Jeff Dinsmore

This software is licensed under the GNU General Public License.