Datasets:

solsticestudioai
/

solstice-residential-energy-pack

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 as a free sample of a larger commercial pack. 100% synthetic. No real customer, meter, or utility records.

What is included

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

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



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 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 — ~25K records, one subject, 72-hour delivery.

Full pack + enterprise scope:

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 — available via Datarade / Monda.

Citation



@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}


}