A novel reinforcement learning-based approach for short-term load and price forecasting in energy markets
Yue Wu, Yin Ma & Hamdolah Aliev
Published: January 30, 2026 - Scientific Reports
Executive Impact
This research introduces a cutting-edge Deep Reinforcement Learning (DRL) approach for short-term load and price forecasting in energy markets. By modeling forecasting as a Markov Decision Process (MDP) and utilizing a Deep Q-Network (DQN), the method demonstrates a remarkable 15-20% reduction in Mean Absolute Percentage Error (MAPE) compared to traditional baselines like ARIMA, LSTM, and XGBoost. The DRL agent learns adaptive prediction policies from historical and real-time data, effectively balancing load and price errors. This leads to enhanced operational efficiency, smarter resource utilization, and increased reliability in smart energy networks. The approach's ability to internalize complex economic dynamics, such as demand response to high prices, highlights its potential for dynamic and intelligent energy market management, validated through simulations on the PJM Interconnection dataset.
0%
MAPE Reduction
0
Price Forecast Accuracy
0
Training Time
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Reinforcement Learning & Time Series Forecasting
This paper leverages Deep Reinforcement Learning (DRL) for critical short-term load and price forecasting in dynamic energy markets. It models the forecasting task as a Markov Decision Process (MDP) and employs a Deep Q-Network (DQN) to learn optimal prediction policies, demonstrating significant accuracy improvements over traditional methods. The core innovation lies in its adaptive learning capabilities from real-time market data.
15-20%
Reduction in MAPE over baselines like ARIMA, LSTM, and XGBoost.
Optimal Policy Learned
DRL agent learned economic principles, predicting demand reduction with high historical prices.
DRL-Based Forecasting Cycle
Data Collection
→
Data Preprocessing
→
State Formation
→
Action Selection (DQN)
→
Evaluation & Reward
→
Policy Update (Q-Learning)
→
Prediction Output
| Model | Load MAE (MW) | Price MAPE (%) |
|---|---|---|
| ARIMA | 250 | 5.6 |
| LSTM | 180 | 4.5 |
| XGBoost | 200 | 4.8 |
| Proposed DRL | 150 | 3.8 |
The proposed DRL model significantly outperforms traditional methods in both load and price forecasting, demonstrating superior accuracy and adaptability to market conditions.
Real-World Application: PJM Interconnection
The DRL model was applied to the PJM Interconnection dataset (2021-2023), encompassing hourly load and electricity prices. It effectively captured complex seasonal and daily patterns, including demand peaks and troughs. The model's ability to adapt to varying market conditions and internalize economic dynamics showcases its practical applicability for enhanced operational efficiency and resource management in real-world energy markets.
Advanced ROI Calculator
Estimate the potential return on investment for implementing a DRL-based forecasting system in your enterprise.
Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Your Implementation Roadmap
A typical DRL forecasting system implementation follows these key phases to ensure successful integration and optimal performance.
Phase 1: Discovery & Strategy
Comprehensive analysis of existing forecasting methods, data infrastructure, and business objectives to tailor the DRL solution. Define KPIs and success metrics.
Phase 2: Data Engineering & Model Development
Cleanse, preprocess, and integrate historical and real-time market data. Develop and train the DRL model (DQN) with customized reward functions.
Phase 3: Validation & Optimization
Rigorously test the model against real-world scenarios and baseline methods. Fine-tune hyperparameters and policy for maximum accuracy and efficiency.
Phase 4: Deployment & Monitoring
Integrate the DRL system into your operational environment. Set up continuous monitoring and feedback loops for ongoing learning and performance refinement.
Ready to Optimize Your Energy Market Forecasting?
Unlock the power of Deep Reinforcement Learning to gain a competitive edge. Our experts are ready to help you transform your energy market predictions and achieve significant operational efficiencies.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat