Machine learning-based energy demand forecasting with dynamic pricing and security risk scoring: A big data analytics approach

Öz

Accurate short-term energy demand forecasting is critical for effective grid management, dynamic pricing, and risk assessment in modern smart energy systems. This study proposes an integrated data-driven framework that combines Long Short-Term Memory (LSTM) neural network-based demand forecasting, rule-based dynamic pricing, and deviation-based security risk scoring. The LSTM model is trained using a sliding window approach, leveraging the previous 24 hours of consumption data to predict the next hour’s demand, and optimized with the Adam algorithm to capture nonlinear and temporal dependencies. Forecast performance is evaluated using RMSE, MAE, and MAPE metrics, demonstrating significant improvements over the persistence baseline, particularly during peak demand hours.In addition, the proposed LSTM model is benchmarked against traditional machine learning algorithms, namely Random Forest and Gradient Boosting, to provide a comprehensive comparative evaluation of forecasting performance.  The predicted demand values feed into a dynamic pricing module, where price adjustments are applied according to demand percentiles, effectively balancing grid load and increasing revenue. Additionally, a normalized risk score is calculated based on forecast deviations, enabling early detection of anomalous consumption behaviors. Simulation results indicate that the proposed approach reduces peak-hour demand, enhances revenue, and provides reliable early warning for potential system anomalies. Comparative results show that while benchmark models may achieve competitive absolute error values, the LSTM model provides superior performance in relative error (MAPE), demonstrating higher robustness for energy management applications. Overall, the integrated framework demonstrates the effectiveness of combining advanced forecasting techniques with decision-support modules, offering a scalable and practical solution for short-term energy management in smart grids.

Anahtar Kelimeler

• Energy forecasting
• Machine learning
• LSTM neural networks
• Demand-side management
• Dynamic pricing
• Smart grid optimization
• Risk assessment
• Time series analysis

Kaynakça

1. [1] Alexiadis V, Drakaki M, Tzionas P. LSTM-Based electricity demand forecasting in smart and sustainable hospitality buildings. Electronics 2025; 14(22): 4456.
2. [2] Semmelmann L, Henni S, Weinhardt C. Load forecasting for energy communities: a novel LSTM-XGBoost hybrid model based on smart meter data. Energy Informatics 2022; 5(Suppl 1): 24.
3. [3] Khalid R, Javaid N, Al-Zahrani FA, Aurangzeb K, Qazi E-u-H, Ashfaq T. Electricity load and price forecasting using Jaya-Long Short Term Memory (JLSTM) in smart grids. Entropy 2019; 22(1): 10.
4. [4] Memarzadeh G, Keynia F. Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm. Electric Power Systems Research 2021; 192: 106995.
5. [5] Zafar S, Hashmi SA, Ayub RH, Farooq H. Short-Term Load Forecasting for Smart Grid based on Bidirectional-LSTM Recurrent Neural Network. Preprint 2024.
6. [6] Tayseer M, Talaat M, Zamel AA, Sedhom BE, Elgamal M, Senjyu T, Song D, Ibrahim IM, Elkholy M. Cyber-resilient machine learning framework for accurate individual load forecasting and anomaly detection in smart grids. Scientific Reports 2025; 15: 44054.
7. [7] Wu Y, Sun W, Li Q. Power load forecasting and anomaly detection using a two-stage attention mechanism and deep neural networks. Electric Power Systems Research 2025; 249: 112056.
8. [8] Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid 2017; 10(1): 841–51.

9. [9] Wang Y, Zhang N, Chen X. A short-term residential load forecasting model based on LSTM recurrent neural network considering weather features. Energies 2021; 14(10): 2737.
10. [10] Han M, Fan L. A short-term energy consumption forecasting method for attention mechanisms based on spatio-temporal deep learning. Computers and Electrical Engineering 2024; 114: 109063.
11. [11] Laitsos V, Vontzos G, Bargiotas D, Daskalopulu A, Tsoukalas LH. Data-driven techniques for short-term electricity price forecasting through novel deep learning approaches with attention mechanisms. Energies 2024; 17(7): 1625.
12. [12] Wang X, Yao Z, Papaefthymiou M. A real-time electrical load forecasting and unsupervised anomaly detection framework. Applied Energy 2023; 330: 120279.
13. [13] Siami-Namini S, Tavakoli N, Namin AS, editors. A comparison of ARIMA and LSTM in forecasting time series. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA); 2018: IEEE.
14. [14] Marino DL, Amarasinghe K, Manic M, editors. Building energy load forecasting using deep neural networks. IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society; 2016: IEEE.
15. [15] Breiman L. Random forests. Machine Learning 2001; 45(1): 5–32.
16. [16] Friedman JH. Greedy function approximation: a gradient boosting machine. Annals of Statistics 2001: 1189–232.
17. [17] Hong T, Pinson P, Fan S, Zareipour H, Troccoli A, Hyndman RJ. Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond. International Journal of Forecasting 2016; 32(3): 896–913.
18. [18] Sadaei HJ, e Silva PCdL, Guimaraes FG, Lee MH. Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series. Energy 2019; 175: 365–77.
19. [19] Van der Heijden T, Lago J, Palensky P, Abraham E. Electricity price forecasting in European day ahead markets: A greedy consideration of market integration. IEEE Access 2021; 9: 119954–66.
20. [20] Mohsenian-Rad A-H, Wong VW, Jatskevich J, Schober R, Leon-Garcia A. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid 2010; 1(3): 320–31.
21. [21] Aghaei J, Alizadeh M-I. Demand response in smart electricity grids equipped with renewable energy sources: A review. Renewable and Sustainable Energy Reviews 2013; 18: 64–72.