A comprehensive end-to-end framework for predicting electricity load and demand using statistical, machine learning, and deep learning architectures.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install electricity-forecasting-framework
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install electricity-forecasting-framework using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Electricity Forecasting?
This framework provides developers with the essential tools to build, evaluate, and deploy electricity demand forecasting systems. Designed to integrate seamlessly into workflows involving Openclaw Skills, it covers everything from Short-Term Load Forecasting (STLF) for grid operations to Long-Term Load Forecasting (LTLF) for infrastructure planning. The skill empowers users to utilize sophisticated models like XGBoost, LightGBM, and Temporal Fusion Transformers without rebuilding the underlying pipeline from scratch.
By focusing on technical accuracy and production readiness, this skill ensures that energy traders, grid operators, and data scientists can move from raw data to actionable insights quickly. It emphasizes feature engineering, such as weather integration and temporal lag analysis, which are critical for high-accuracy predictions in the energy sector.
Electricity Forecasting Use Cases
- Managing grid operations and unit commitment through short-term load forecasting.
- Optimizing energy trading strategies by predicting market demand shifts.
- Planning infrastructure investments and capacity expansion using long-term forecasting data.
- Scheduling maintenance and fuel procurement based on medium-term consumption patterns.
How Electricity Forecasting Works
- Data ingestion involves parsing datetime indexes and handling missing values through interpolation or forward-filling.
- Feature engineering creates temporal markers, lag features, and rolling statistics while integrating weather data.
- Model training is performed using the selected architecture, ranging from statistical ARIMA to deep learning Transformers.
- Evaluation is conducted through time-series cross-validation and backtesting to ensure model robustness.
- Production deployment involves model serialization and setting up monitoring for forecast drift.
Electricity Forecasting Setup
To get started with this component of your Openclaw Skills library, follow these steps:
# Install dependencies and prepare your local dataset
python scripts/prepare_data.py --input raw_load.csv --output processed/
# Train a model (e.g., XGBoost) for a 24-hour horizon
python scripts/train_model.py --model xgboost --data processed/ --horizon 24
Electricity Forecasting Data Schema & Taxonomy
The skill expects a structured dataset and generates specific metadata for evaluation. Below is the primary data structure:
| Column | Type | Requirement | Description |
|---|---|---|---|
| timestamp | Datetime | Required | The primary index (hourly or sub-hourly) |
| load | Float | Required | The target variable (MW or kWh) to be predicted |
| temperature | Float | Required | Core weather feature for demand correlation |
| holiday_flag | Boolean | Optional | Marker for public holidays and weekends |
Electricity Forecasting Advanced Features
- Multi-horizon forecasting support for short, medium, and long-term planning scenarios.
- Uncertainty quantification using Quantile Regression and Conformal Prediction for risk assessment.
- Automated hyperparameter optimization using the integrated search scripts for optimal model performance.
- Support for State-of-the-Art (SOTA) deep learning architectures like iTransformer and N-BEATS.
SKILL.md
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