| --- |
| license: cc-by-4.0 |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| - time-series-forecasting |
| language: |
| - en |
| tags: |
| - synthetic |
| - energy |
| - solar |
| - battery |
| - residential-energy |
| - vpp |
| - virtual-power-plant |
| - distributed-energy |
| - tariff-modeling |
| - outage-resilience |
| - analytics |
| - tabular |
| pretty_name: Solstice Residential Energy Pack |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: battery_systems |
| data_files: |
| - split: train |
| path: data/battery_systems/train.csv |
| - config_name: billing_and_savings |
| data_files: |
| - split: train |
| path: data/billing_and_savings/train.csv |
| - config_name: channel_attribution |
| data_files: |
| - split: train |
| path: data/channel_attribution/train.csv |
| - config_name: daily_generation_consumption |
| data_files: |
| - split: train |
| path: data/daily_generation_consumption/train.csv |
| - config_name: dashboard_suggestions |
| data_files: |
| - split: train |
| path: data/dashboard_suggestions/train.csv |
| - config_name: dispatch_events |
| data_files: |
| - split: train |
| path: data/dispatch_events/train.csv |
| - config_name: households |
| data_files: |
| - split: train |
| path: data/households/train.csv |
| - config_name: installation_pipeline |
| data_files: |
| - split: train |
| path: data/installation_pipeline/train.csv |
| - config_name: metric_definitions |
| data_files: |
| - split: train |
| path: data/metric_definitions/train.csv |
| - config_name: outage_events |
| data_files: |
| - split: train |
| path: data/outage_events/train.csv |
| - config_name: portfolio_kpis_daily |
| data_files: |
| - split: train |
| path: data/portfolio_kpis_daily/train.csv |
| - config_name: service_tickets |
| data_files: |
| - split: train |
| path: data/service_tickets/train.csv |
| - config_name: solar_systems |
| data_files: |
| - split: train |
| path: data/solar_systems/train.csv |
| - config_name: subscriber_contracts |
| data_files: |
| - split: train |
| path: data/subscriber_contracts/train.csv |
| - config_name: utility_tariffs |
| data_files: |
| - split: train |
| path: data/utility_tariffs/train.csv |
| - config_name: vpp_program_enrollment |
| data_files: |
| - split: train |
| path: data/vpp_program_enrollment/train.csv |
| --- |
| |
| # Solstice Residential Energy Pack (Sample) |
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| **A synthetic residential solar-plus-storage operations dataset for VPP dispatch, tariff-aware savings, billing, and outage resilience.** This sample is designed for product demos, analytics workflows, dashboard prototyping, and AI model validation where real customer or utility data is unavailable or too sensitive to use. |
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| Built by [SolsticeAI](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic. No real customer, meter, or utility records. |
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| ## What is included |
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| | File | Rows | Grain | Purpose | |
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| |---|---:|---|---| |
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| | `households.csv` | 500 | household | Household archetypes, geography, electrification, and outage risk | |
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| | `daily_generation_consumption.csv` | 90,000 | date x household | Load, solar generation, import/export, battery usage, and daily savings | |
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| | `dispatch_events.csv` | 5,819 | dispatch event | Requested vs delivered dispatch, participation, incentives, and grid value | |
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| | `billing_and_savings.csv` | 3,000 | month x household | Counterfactual bills, subscription payments, credits, and net customer value | |
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| | `metric_definitions.csv` | 3 | metric | Metric formulas and table-level documentation | |
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| | `dashboard_suggestions.csv` | 3 | chart | Starter dashboard recipes for product and analytics teams | |
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| **Coverage:** USA |
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| **Period:** 6 months (`2025-01-01` to `2025-06-29`) |
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| **Join key:** `household_id` |
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| **Formats in this sample repo:** CSV |
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| ## Why this dataset is useful |
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| Most public solar or energy datasets are either too generic, too narrow, or detached from the operating model of a residential energy business. This sample is shaped around the questions a solar-plus-storage platform, VPP operator, DERMS vendor, or energy analytics team actually cares about: |
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| - Which household profiles create the highest dispatch value? |
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| - How much do tariff design and load shape affect savings? |
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| - Which homes deliver the most outage resilience value? |
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| - How reliable is dispatch participation across a residential fleet? |
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| - How do billing, credits, and contract economics affect customer value? |
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| ## What makes the sample credible |
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| - Stable relational keys and business-readable tables |
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| - Daily operational energy facts rather than flat summary rows |
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| - Dispatch, billing, and savings data tied to the same household base |
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| - Structured for dashboarding, workflow testing, demos, and model development |
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| - Synthetic by design, so it can be shared safely across internal and external teams |
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| ## Typical use cases |
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| - Residential energy product demos |
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| - VPP dispatch and participation analytics |
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| - Tariff-aware savings analysis |
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| - Billing workflow and customer-value testing |
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| - Outage resilience reporting |
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| - AI model validation on structured energy operations data |
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| - Dashboard and BI template development |
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| ## Quick start |
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| ```python |
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| import pandas as pd |
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| households = pd.read_csv("data/households/train.csv") |
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| daily = pd.read_csv("data/daily_generation_consumption/train.csv", parse_dates=["date"]) |
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| dispatch = pd.read_csv("data/dispatch_events/train.csv", parse_dates=["date"]) |
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| billing = pd.read_csv("data/billing_and_savings/train.csv") |
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| # Example: average savings by state |
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| savings_by_state = ( |
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| daily.merge(households[["household_id", "state"]], on="household_id", how="left") |
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| .groupby("state")["customer_savings_usd"] |
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| .mean() |
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| .reset_index() |
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| ) |
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| ``` |
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| ## Schema |
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| See [SCHEMA.md](./SCHEMA.md) for the full field definitions and pack design. |
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| See `manifest.json` for sample generation metadata and row counts. |
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| ## License |
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| Released under **CC BY 4.0**. Use freely for demos, internal tooling, research, education, and commercial prototyping with attribution. |
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| Synthetic data only. No real customer, patient, meter, or utility information. |
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| ## Get the full pack |
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| This Hugging Face repo is a **500-household, 6-month sample**. The production pack scales to 5,000–25,000+ households, 12+ month historical windows, additional tables (tariffs, outage events, service tickets, contracts, installations, enrollment, portfolio KPIs), CSV and Parquet delivery, and buyer-specific variants. |
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| **Self-serve (Stripe checkout):** |
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| - [**Sample Scale tier — $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) — ~25K records, one subject, 72-hour delivery. |
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| **Full pack + enterprise scope:** |
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| - [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) — per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants. |
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| **Procurement catalog:** |
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| - [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) — available via Datarade / Monda. |
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| ## Citation |
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| ```bibtex |
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| @dataset{solstice_residential_energy_pack_2026, |
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| title = {Solstice Residential Energy Pack (Sample)}, |
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| author = {SolsticeAI}, |
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| year = {2026}, |
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| publisher = {Hugging Face}, |
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| url = {https://huggingface.co/datasets/solsticestudioai/solstice-residential-energy-pack} |
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| } |
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| ``` |
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