Energy Load Forecasting Platform for Power Trading

Overview

The team needed more reliable day-ahead load forecasts to improve trading decisions and reduce exposure to price volatility. We delivered a modular forecasting platform with a React frontend and FastAPI forecasting endpoints, with a PySpark-based ingestion and an ML pipeline for time-series prediction. The solution achieved 7.48% MAPE in rolling backtests and as low as ~1% error in a final-split experiment, improving forecast quality and operational confidence.

Challenge

Before this solution, forecasting workflows were too fragile for high-volatility market conditions.

• Signals changed quickly across hour, day, season, and weather cycles.
• Ingestion and transformation needed stronger scale characteristics for large historical energy and weather datasets.
• Existing approaches were not consistent enough for risk-aware energy trading decisions.
• Teams lacked a unified forecasting workflow with transparent model comparison, model tracking, and drift visibility.

If unresolved, forecast error would continue to affect hedging, procurement timing, and intraday decision quality.

Solution

We implemented a modular forecasting architecture designed for fast iteration and production-readiness.

• Architecture pattern: A clean pipeline split into PySpark ingestion, feature engineering, model training, backtesting, and API-serving layers.
• Model choices: We benchmarked ARIMA, Prophet, and LSTM models on proprietary data and selected model behavior based on target accuracy and stability.
• Integration approach: FastAPI endpoints expose forecast and model outputs to a React dashboard for analysts and engineering stakeholders.
• MLOps controls: MLflow tracked experiments and model versions, while Evidently and Great Expectations monitored data quality, data drift, and model drift.
• Tradeoffs: We balanced interpretability and performance by keeping classical statistical models for baseline visibility while using LSTM for stronger non-linear time-series accuracy.

Key Features

1. Unified energy load forecasting API for day-ahead predictions.
2. Multi-model benchmarking with consistent evaluation metrics.
3. Quantile-aware forecasting outputs to support risk-sensitive decision making.
4. Data ingestion and transformation layer for scalable time-series processing.
5. Model lineage and experiment tracking for governed releases.
6. Data drift and model drift monitoring with threshold-based alerting.
7. Automated pipeline orchestration for reproducible retraining and backtesting.
8. React-based forecast interface for technical and operational users.

Technical Implementation

Backend & Infrastructure

The backend was structured as FastAPI forecast endpoints over a Python forecasting engine. Pipeline execution was organized as modular services so PySpark ingestion, training, and inference could run independently. This made it straightforward to schedule retraining, test parameter changes, and expose prediction endpoints without tightly coupling model code to UI concerns.

Data & AI Components

The model layer used a shared interface and three forecasting strategies:

• ARIMA for statistical baseline behavior.
• Prophet for trend/seasonality decomposition.
• LSTM for non-linear sequence learning with richer feature interactions.

Feature engineering included temporal signals, generation-side signals, and derived demand-generation deltas. Evaluation used rolling backtesting and standard error metrics to keep comparisons consistent across models and time windows.

For governance, we implemented MLflow-based experiment tracking and versioned model promotion, plus drift checks that compare live feature and prediction distributions against training baselines. Data quality rules were enforced before training and before model promotion to production endpoints.

Frontend & User Experience

We delivered a React interface focused on forecast readability and speed of analysis. Users can review historical series, trigger forecast runs, and compare predicted versus actual trajectories without switching tools. The frontend consumed typed FastAPI responses, keeping UI behavior deterministic and easy to evolve.

Security & Reliability

Given NDA constraints and operational sensitivity, implementation emphasized controlled data handling and reproducible runs. Forecast outputs were generated through deterministic pipelines, and model comparison remained auditable through explicit backtest metrics rather than opaque one-off reporting. Drift and quality alerts were integrated into release checks so degraded models could be held back or rolled back before impacting trading workflows.

Results

• 7.48% MAPE with LSTM in rolling backtests, outperforming ARIMA (13.57%) and Prophet (8.90%) on the same evaluation frame.
• 83% reduction in error between short-window backtest and full-data final split experiments.
• ~1% error in a final-split run on full historical coverage, showing upper-bound potential when complete seasonal history is available.
• Stronger forecast consistency for energy trading analytics, supporting more informed day-ahead planning and risk management.

Technology Stack

• AI/ML: PySpark, TensorFlow, Keras, Prophet, ARIMA, scikit-learn
• Backend: Python, FastAPI
• Frontend: React
• Infrastructure: MLflow, Evidently, Great Expectations