💻 computer science

BQEB ForecastBench: Benchmarking AI Models for Smart Grid Forecasting Using BQEB-Data v1

This paper introduces BQEB ForecastBench, an open benchmarking framework utilizing the BQEB-Data v1 dataset to standardize the evaluation of AI models for smart-grid forecasting, demonstrating through baseline experiments that Linear Regression outperforms other models in predicting electricity load and prices while highlighting the inherent complexity of price forecasting.

Original authors: Rakesh Kumar Agrawal

Published 2026-07-29

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CC BY 4.0

Original authors: Rakesh Kumar Agrawal

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the power grid not as a giant, silent machine, but as a bustling, chaotic city where electricity is the currency. In this city, the "load" is how much everyone is spending at once, and the "price" is how much that currency costs at any given second. For a long time, predicting this spending was like trying to guess the weather by looking at a single cloud; it was hard, messy, and everyone used their own private maps and tools to make their guesses. This made it nearly impossible to tell if one scientist's "super-predictor" was actually better than another's, or if they just had a lucky map. To fix this, researchers needed a common playing field—a standard test track where every new prediction engine could be run side-by-side using the exact same fuel, the same track, and the same stopwatch. This is the world of "smart grid forecasting," where the goal is to use computers to see the future of energy so the lights stay on and the bills stay fair.

Enter the BQEB ForecastBench, a new "race track" for artificial intelligence (AI) designed to solve this exact problem. Think of it as a standardized video game level where every AI model has to play the same two levels: predicting how much electricity the city will need in the next hour, and guessing what the price of electricity will be tomorrow. The creators of this benchmark didn't just build the track; they also provided the "game data" (called BQEB-Data v1), which is a massive, public collection of numbers about weather, solar power, electric cars, and battery levels, all organized in a neat, time-ordered line.

The paper puts three classic "player characters" into this race to see how they perform. These aren't the flashy, super-complex AI robots you might hear about in sci-fi movies; instead, the researchers chose three reliable, workhorse models: Linear Regression (a simple model that draws a straight line through the data), Random Forest (a team of decision-makers voting on the answer), and Gradient Boosting (a student who learns by correcting its own mistakes). The results were surprising to some: the simplest character, Linear Regression, actually won the race. For predicting the next hour's energy load, it was incredibly accurate, with an error of just 9.94 MW (megawatts) and a score of 0.9288 on a scale of how well it fits the data. However, the second race—predicting tomorrow's electricity price—was much harder. Even the winner struggled here, with an error of 4.39 USD/MWh and a fit score of only 0.2078. This tells us that while we can pretty well guess how much energy people will use, guessing the price is like trying to predict the stock market; it's wild, chaotic, and full of surprises.

The most important takeaway isn't just that the simple model won, but that we now have a fair, open, and repeatable way to test any future AI. Before this, researchers might have used different data or different rules, making it impossible to know who was truly the best. Now, anyone can download the same data, run their own fancy new AI, and see exactly how it stacks up against the baseline. The paper suggests that while complex, deep-learning models might eventually beat these simple ones, we first need this solid, shared foundation to make sure we are all comparing apples to apples. It's a step toward a future where our energy systems are smarter, more reliable, and built on truth rather than guesswork.

Technical Summary: BQEB ForecastBench

Problem Statement

The rapid integration of renewable energy, distributed resources, battery storage, and electric vehicles has increased the complexity of smart grid operations, necessitating accurate forecasting for demand, generation, and market prices. While artificial intelligence (AI) and machine learning (ML) have shown promise in these areas, the field suffers from a critical lack of standardization. Existing research often relies on proprietary datasets, inconsistent feature engineering, heterogeneous experimental protocols, and varying evaluation metrics. These discrepancies hinder the objective comparison of forecasting models, limit reproducibility, and prevent the establishment of fair baselines for emerging AI techniques. Unlike domains such as computer vision or natural language processing, the energy forecasting community lacks widely adopted, open benchmark frameworks that combine public data, standardized tasks, and transparent evaluation procedures.

Methodology

To address these gaps, the paper introduces BQEB ForecastBench, an open benchmarking framework built upon the publicly available BQEB-Data v1 dataset. The methodology is designed to prioritize reproducibility and transparency over the introduction of novel algorithms.

Dataset and Preprocessing

The foundation of the benchmark is BQEB-Data v1, a multivariate time-series dataset containing 10,512 records collected at 15-minute intervals from January to April 2026. The dataset includes 37 features covering:

Operational Variables: Electricity load (total and segmented), battery state-of-charge, and storage power.
Market Variables: Electricity prices.
Renewable & Environmental: Solar and wind generation, temperature, humidity, wind speed, and solar irradiance.
Emerging Loads: Electric vehicle (EV) charging demand.
System & Security: Grid frequency, voltage, line losses, and cybersecurity indicators (anomaly scores, alert flags).

The data undergoes a standardized preprocessing pipeline involving quality validation, missing value handling (resulting in 0% missing values), temporal alignment, and normalization. Crucially, the framework employs chronological partitioning (70% training, 15% validation, 15% testing) rather than random shuffling to prevent temporal data leakage and simulate real-world forecasting conditions.

Forecasting Tasks

The benchmark defines two operationally significant supervised regression tasks:

Next-Hour Electricity Load Forecasting: Predicting total system demand for the immediate following hour.
Day-Ahead Electricity Price Forecasting: Predicting wholesale market prices for the next trading day.

Baseline Models and Evaluation

Three representative classical machine learning models were selected to establish reference performance:

Linear Regression (LR): Chosen for simplicity, interpretability, and computational efficiency.
Random Forest (RF): An ensemble method selected for its ability to capture nonlinear interactions.
Gradient Boosting (GBR): An iterative ensemble method for modeling complex nonlinear relationships.

Note: Deep learning architectures (e.g., LSTM, Transformers) were intentionally excluded from this initial release to maintain a lightweight, reproducible baseline, with plans to incorporate them in future versions.

Performance was evaluated using four standard metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination ( $R^2$ ). All experiments were conducted in Python using scikit-learn on a standard workstation, ensuring the framework is accessible without high-performance computing infrastructure.

Key Results

The experimental evaluation yielded the following findings:

1. Next-Hour Load Forecasting:
All baseline models demonstrated strong predictive capability for electricity demand. Linear Regression achieved the best overall performance, outperforming the ensemble methods:

RMSE: 9.94 MW
$R^2$ : 0.9288
MAPE: 2.13%

2. Day-Ahead Price Forecasting:
Price prediction proved significantly more challenging due to market volatility and stochastic factors. While accuracy was lower than load forecasting, Linear Regression again provided the strongest baseline:

RMSE: 4.39 USD/MWh
$R^2$ : 0.2078
MAPE: 7.84%

3. Comparative Analysis:
The results highlight a distinct difference in complexity between the two tasks. The high $R^2$ values for load forecasting suggest that demand exhibits stable temporal patterns well-captured by linear relationships in this dataset. In contrast, the lower $R^2$ for price forecasting reflects the inherent difficulty of modeling the nonlinear, dynamic factors influencing electricity markets. Notably, the simpler Linear Regression model outperformed more complex ensemble methods (Random Forest and Gradient Boosting) in both tasks, suggesting that the feature representation in BQEB-Data v1 possesses strong linear characteristics for these specific horizons.

Significance and Contributions

The paper positions BQEB ForecastBench not as a state-of-the-art forecasting solution, but as a foundational, reproducible research resource. Its primary contributions include:

Standardized Benchmarking: It establishes the first open framework in this specific domain that combines a public dataset (BQEB-Data v1), standardized chronological protocols, and transparent evaluation metrics.
Reproducibility: By providing the dataset, preprocessing code, and baseline implementations, the framework enables independent validation and fair comparison of future models, addressing the "black box" nature of many existing energy forecasting studies.
Modular Extensibility: The framework is designed to be extensible. While the current release focuses on classical ML baselines, it provides the necessary infrastructure for future researchers to evaluate deep learning, transformer-based, graph neural network, and hybrid AI models under identical conditions.
Practical Utility: The benchmark offers utilities, Independent System Operators (ISOs), and researchers a standardized environment to test algorithms before operational deployment or to compare new methodologies against established baselines.

The authors conclude that BQEB ForecastBench bridges the gap between available smart-grid data and objective evaluation methodologies, fostering the development of trustworthy and reproducible AI for next-generation energy systems.