| ---
|
| tags:
|
| - tabular
|
| - energy
|
| - forecasting
|
| - weather
|
| - tsfile
|
| - modality:timeseries
|
| pretty_name: REG-Forecasting_v2
|
| size_categories:
|
| - 100K<n<1M
|
| modality:
|
| - timeseries
|
| ---
|
|
|
| # REG-Forecasting_v2 (TsFile)
|
|
|
| Apache TsFile version of
|
| [`wachawich/REG-Forecasting_v2`](https://huggingface.co/datasets/wachawich/REG-Forecasting_v2).
|
|
|
| ## Overview
|
|
|
| Renewable Energy Generation (REG) forecasting data at **hourly** granularity, spanning
|
| **2020-01-01 to 2025-11-19**. For each hour it records the generation (`value`) of two
|
| generation types together with the shared meteorological and solar-geometry features.
|
| Compared with the v1 dataset, this version adds extra columns such as `solar_count`,
|
| `wind_turbine_count`, and cyclic time encodings (`sin_time`, `cos_time`,
|
| `day_of_month_sin/cos`, `month_of_year_sin/cos`).
|
|
|
| - **Two generation types:** `type_name` = `Solar` / `Wind`.
|
| - **Forecast target:** `value` — generation in that hour.
|
| - **Features:** radiation, cloud cover, temperature, humidity, pressure, wind
|
| speed/direction, precipitation, sunrise/sunset times, and cyclic calendar encodings.
|
|
|
| ## Schema (TsFile structure)
|
|
|
| - **Time** (INT64, milliseconds) — the hourly timestamp.
|
| - **type_name** (TAG) — the device dimension, `Solar` / `Wind`. Query one type with
|
| `WHERE type_name='Solar'`.
|
| - All remaining columns are FIELDs: `value` plus the meteorological and calendar features.
|
|
|
| Type mapping: floating-point → DOUBLE, integers → INT64, strings → STRING. A small number
|
| of byte-for-byte duplicate rows (from daylight-saving transitions) were removed so that a
|
| single device never carries two points at the same timestamp.
|
|
|
| ## Usage
|
|
|
| Read `data.tsfile` with the Apache TsFile Java or Python SDK.
|
|
|
| ## Source & license
|
|
|
| - Original dataset: https://huggingface.co/datasets/wachawich/REG-Forecasting_v2
|
| - Author: wachawich
|
| - License: not declared by the original dataset card; please defer to the original dataset.
|
| |
