Energy Usage Forecasting Model Based on Long Short-Term Memory (LSTM) and eXplainable Artificial Intelligence (XAI)

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A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

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A Deep Learning Based Hybrid Framework for Day-Ahead Electricity Price Forecasting

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A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

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Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs

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Research on Network Behavior Anomaly Analysis Based on Bidirectional LSTM

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Explainable artificial intelligence for energy systems maintenance: A review on concepts, current techniques, challenges, and prospects

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1D Convolutional LSTM-based wind power prediction integrated with PkNN data imputation technique

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Decoding the Intellectual Landscape and Evolving Research Paradigm of Artificial Intelligence in Environmental Sustainability

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Hybrid CNN-LSTM model for urban energy load forecasting with IGA-XAI for smart grids: Peak and off-peak variability insights

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Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review

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Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning

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Design of an IoT model for forecasting energy consumption of residential buildings based on improved long short-term memory (LSTM)

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eXplainable artificial intelligence (XAI) in business management research: a success/failure system perspective

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An explainable artificial intelligence feature selection framework for transparent, trustworthy, and cost-efficient energy forecasting

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Application of SHAP and Multi-Agent Approach for Short-Term Forecast of Power Consumption of Gas Industry Enterprises

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Explainable and secure wind power forecasting: SHAP-enhanced framework with feature stability, anomaly detection, and adversarial robustness

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Energy-efficient transfer learning for water consumption forecasting

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Deep Learning Method to Analyze the Bi-LSTM Model for Energy Consumption Forecasting in Smart Cities

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An Interpretable Framework for Stock Market Forecasting Using Long Short-Term Memory (LSTM) Networks with SHAP - Explainable AI (XAI) Method

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Statistical and Machine Learning Approaches for Electrical Energy Forecasting

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Analysis of Statistical and Deep Learning Techniques for Temperature Forecasting

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Business Purchase Prediction Based on XAI and LSTM Neural Networks

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A Novel Machine Learning Approach to Optimize Energy Forecasting in Industry

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Forecasting Civil Unrest in South Africa Using Social Media Data: A Hybrid Machine Learning Approach

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Building LSTM based forecast model with pattern exploration of load profiles for enhancing energy efficiency in electrical laboratories

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Explainable Multi-Frequency Long-Term Spectrum Prediction Based on GC-CNN-LSTM

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Thermal comfort-constrained HVAC flexibility prediction in institutional buildings using hybrid physics and LSTM modelling

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Hybrid Vector Auto Regression and Tree-Based Ensembles for Energy Load Forecasting: A Case Study

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An Explainable Approach to Land-Use Classification Using CNN-LSTM and LIME

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Photovoltaic Cell Power Forecasting Using LSTM with XAI Integration

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Forecasting Trends in Electrical Energy Efficiency in the Food Industry

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Towards Explainable Machine Learning NIDS Reflecting Cyber-Attack Characteristics

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Explainable Singular Spectrum Analysis deep learning model for half-hourly electricity price prediction

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AI-driven forecasting of the world energy index: The role of Iraq’s GDP in global energy interconnection

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A dual-stage LSTM framework for accurate prediction of bearing remaining useful life under shaft misalignment conditions

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A Comparative Analysis between K-Means and Agglomerative Clustering Techniques in Maritime Skill Certification

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Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation

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Deep Learning Techniques for Wind Speed Forecasting at Palembang Airport

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An Evidence-Based Explainable AI Approach for Analyzing the Influence of CO$$_{2}$$ Emissions on Sustainable Economic Growth

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Energy Consumption Forecasting using LSTM-based Deep Learning Models

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Smart Audio Surveillance System for Real-Time Violence Detection and Alarm Response Using Long Short-Term Memory

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A Stacking-Based Multi-Step LSTM and Policy-Enhanced SVR Method for Carbon Emission Prediction

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Artificial intelligence-driven sustainable resource allocation strategies for green economy

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Smart Decision Support for Uncertainty and Disruptions in Industry 4.0 Operations

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Smart integration of aerosols for DNI predictions in Australia using LSTM

Hu, Y., Prasad, A., & Kay, M. J. (2026) · Solar Energy · doi:10.1016/j.solener.2026.114855

Data-driven energy monitoring and prediction through an online visual platform for discrete manufacturing workshop

Liu, D., Ma, L., Yi, X., Li, C., & Huang, S. (2026) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2026.2699314

Comparative performance analysis of LSTM and classical machine learning models for IoT-based short-term energy consumption forecasting

JOBANI, A. E., & Boyacı, A. (2026) · International Journal of Energy Studies · doi:10.58559/ijes.1814680

VMD-LSTM-transformer: A hybrid approach for robust solar irradiance forecasting in complex terrains with application to photovoltaic systems

Sarango, K., Maldonado-Correa, J., Torres-Cabrera, J., & Samaniego, C. (2026) · Next Energy · doi:10.1016/j.nxener.2026.100855

Explainable and Generative AI for Smart Grids: A Unified Framework and Research Directions

Thakur, S., Malik, M., & Gupta, R. (2026) · International Conference on Circuit, Power and Computing Technologies · doi:10.1109/iccpct70290.2026.11654803

Simulation-Validated Optimization of a Lithium-Ion Battery Solar PV Microgrid for Rural Electrification in Nigeria

Adesina, L. M., Ariyo, B. O., Ogunbiyi, O., & Kote, E. (2026) · Nile Journal of Engineering and Applied Science · doi:10.5455/njeas.324954

Machine learning for predictive maintenance in energy storage systems: A systematic review of health prediction, fault diagnosis, and deployment challenges

Usman, M., Dutta, M., Hasan, M. R., Rim, S. I., Mahmud, M. Z. A., Mobarak, M. H., Refati, F. A. D., & Rimon, I. H. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124507

Toward Industrial World Models: A Review of Multimodal Fusion for Time Series and Text in IIoT

Chen, C., Chen, W., Wang, T., Wang, Z., Chen, Z., Shao, H., & Leng, J. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3734499

ENTRIQ: Smart Energy Optimization with Explainable AI

Krishnan, S. R., Alias, A., Davis, D., L, G. K. T., Joy, J., & Sajin, M. S. (2026) · International Conference on Innovations in Engineering and Technology (ICIET) · doi:10.1109/iciet70550.2026.11712741

An explainable AI-driven multi-objective framework for intelligent renewable home energy management and optimal power scheduling

Alsolami, E. (2026) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2026.101497

Energy Consumption Prediction Based on LightGBM Empowered With eXplainable Artificial Intelligence

Munir, S., Pradhan, M. R., Abbas, S., & Khan, M. A. (2024) · IEEE Access · doi:10.1109/access.2024.3418967 · Cited by 35 · see citing works

Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble

Alghamdi, M. A., ALGhamdi, A. S. A.-M., & Ragab, M. (2024) · Big Data Mining and Analytics · doi:10.26599/bdma.2023.9020030 · Cited by 27 · see citing works

Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) Network Models for Forecasting Energy Consumptions

Shariff, S. M. (2022) · European Journal of Electrical Engineering and Computer Science · doi:10.24018/ejece.2022.6.3.435 · Cited by 17 · see citing works

Optimized LSTM Model for Electric Load Forecasting using Deep Learning with Genetic Algorithm

Balraj, E., Pugalendhi, T., SureshKumar, M., & Vijayasarathi, K. (2023) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs56967.2023.10142580 · Cited by 1 · see citing works

eXplainable AI (XAI)-Based Input Variable Selection Methodology for Forecasting Energy Consumption

Sim, T., Choi, S., Kim, Y.-J., Youn, S. H., Jang, D.-J., Lee, S., & Chun, C.-J. (2022) · Electronics · doi:10.3390/electronics11182947 · Cited by 38 · see citing works