---
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)





**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.





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.





## What is included





| File | Rows | Grain | Purpose |


|---|---:|---|---|


| `households.csv` | 500 | household | Household archetypes, geography, electrification, and outage risk |


| `daily_generation_consumption.csv` | 90,000 | date x household | Load, solar generation, import/export, battery usage, and daily savings |


| `dispatch_events.csv` | 5,819 | dispatch event | Requested vs delivered dispatch, participation, incentives, and grid value |


| `billing_and_savings.csv` | 3,000 | month x household | Counterfactual bills, subscription payments, credits, and net customer value |


| `metric_definitions.csv` | 3 | metric | Metric formulas and table-level documentation |


| `dashboard_suggestions.csv` | 3 | chart | Starter dashboard recipes for product and analytics teams |





**Coverage:** USA  


**Period:** 6 months (`2025-01-01` to `2025-06-29`)  


**Join key:** `household_id`  


**Formats in this sample repo:** CSV





## Why this dataset is useful





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:





- Which household profiles create the highest dispatch value?


- How much do tariff design and load shape affect savings?


- Which homes deliver the most outage resilience value?


- How reliable is dispatch participation across a residential fleet?


- How do billing, credits, and contract economics affect customer value?





## What makes the sample credible





- Stable relational keys and business-readable tables


- Daily operational energy facts rather than flat summary rows


- Dispatch, billing, and savings data tied to the same household base


- Structured for dashboarding, workflow testing, demos, and model development


- Synthetic by design, so it can be shared safely across internal and external teams





## Typical use cases





- Residential energy product demos


- VPP dispatch and participation analytics


- Tariff-aware savings analysis


- Billing workflow and customer-value testing


- Outage resilience reporting


- AI model validation on structured energy operations data


- Dashboard and BI template development





## Quick start





```python


import pandas as pd





households = pd.read_csv("data/households/train.csv")


daily = pd.read_csv("data/daily_generation_consumption/train.csv", parse_dates=["date"])


dispatch = pd.read_csv("data/dispatch_events/train.csv", parse_dates=["date"])


billing = pd.read_csv("data/billing_and_savings/train.csv")





# Example: average savings by state


savings_by_state = (


    daily.merge(households[["household_id", "state"]], on="household_id", how="left")


         .groupby("state")["customer_savings_usd"]


         .mean()


         .reset_index()


)


```





## Schema





See [SCHEMA.md](./SCHEMA.md) for the full field definitions and pack design.  


See `manifest.json` for sample generation metadata and row counts.





## License





Released under **CC BY 4.0**. Use freely for demos, internal tooling, research, education, and commercial prototyping with attribution.





Synthetic data only. No real customer, patient, meter, or utility information.





## Get the full pack





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.





**Self-serve (Stripe checkout):**


- [**Sample Scale tier — $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) — ~25K records, one subject, 72-hour delivery.





**Full pack + enterprise scope:**


- [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.



**Procurement catalog:**


- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) — available via Datarade / Monda.





## Citation





```bibtex


@dataset{solstice_residential_energy_pack_2026,


  title        = {Solstice Residential Energy Pack (Sample)},


  author       = {SolsticeAI},


  year         = {2026},


  publisher    = {Hugging Face},


  url          = {https://huggingface.co/datasets/solsticestudioai/solstice-residential-energy-pack}


}


```