Traditional forecasting methods struggle to capture the complex nonlinear relationships and long-term temporal dependencies inherent in heat load data

The gap

Evidence profile

Sourced from the stated research gap and abstract and future work and recommendations of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 6 journals. Those papers have been cited 43 times in total.

Supporting evidence — 6 representative gaps

• Multi-Task Time Series Forecasting of Plant Load Losses: A Comparative Study of a Temporal Fusion Transformer and LSTM (2026) · Applied System Innovation · doi

  Much of the existing literature focuses on electricity demand forecasting, renewable energy forecasting, and consumption prediction, with limited attention given to forecasting load losses within electricity generation. Relatively little research has studied how the comparative performance of TFT and LSTM changes as training dataset size increases, particularly in medium-term forecasting applications.

  generalstated research gap
  Keywords: much existing literature focuses electricity demand forecasting renewable
• Machine Learning Applications for Smart Building Energy Utilization: A Survey (2024) · Archives of Computational Methods in Engineering · cited 28× · doi

  The contributions of this paper are (a) a systematic review of applications and machine learning methods for smart building energy utilization, (b) a novel taxonomy for the applications, (c) detailed analysis of these solutions and techniques used for the applications (electric grid, smart building energy management and control, maintenance and security, and personalization), and, finally, (d) a discussion on open issues and developments in the field.

  generalabstractevidence 5/5
  Keywords: applications smart building energy contributions systematic review machine learning utilization novel taxonomy detailed solutions techniques
• AI-Driven Forecasting of Sustainable Energy Transitions Using Multi-Factor Green Index Modelling (2026) · International Journal of Mathematics And Computer Research · doi

  1. Integration of Satellite and IoT Data: Including real-time data on emissions, temperature, and renewable output to enhance model accuracy. 2. Explainable AI (XAI): Implementing interpretable models to better understand how each sustainability factor influences the IGTI. 3. Country-Specific Policy Forecasting: Customizing the model for national-level forecasting with localized socio-economic parameters. 4. Hybrid Deep Learning Models: Combining LSTM with attention mechanisms or graph neural networks for improved temporal and spatial insights. 5. Sustainability Simulation Dashboard: Developing an interactive platform where policymakers can visualize and test the effects of various energy scenarios. 14. APPLICATIONS ● Energy Planning Agencies: Accurate forecasts help in designing future renewable energy policies and transition plans. ● Power and Utility Companies: Predictive insights support better grid management and integration of clean energy sources. ● Environmental Organizations: Forecasting helps track progress toward carbon-neutral and climate-resilient goals. ● Government and Policy Makers: The Green Index supports evidence-based decisions for sustainable energy strategies. ● Research and Academic Institutions: The model provides a reliable tool for studying long-term energy trends and sustainability performance. 15. SCOPE ● The model can be used to study and predict future renewable energy trends up to the year 2035. ● It helps identify the major factors that influence a country’s shift toward clean and sustainable energy. ● The research framework can be applied to different countries, regions, or global datasets. ● The Green Energy Index can be expanded by adding new indicators like energy storage, EV adoption, or smart grid data. ● The AI models used in the study can be improved with more real-time data for even more accurate predictions. the research AUTHOR CONTRIBUTIONS ● Bharati Patil conceptualized idea, developed the AI-based forecasting framework, designed the multi-factor Green Energy Index model, and contributed to the interpretation of the experimental findings. ● Komal Korade carried out an extensive literature review, refined the problem statement, and assisted in structuring the overall research methodology. ● Deepashree Mehendale performed data pre-processing, model training, result analysis, and supported the evaluation of the experimental results. ● All authors jointly contributed to writing, editing, and proofreading the manuscript. They have reviewed and approved the final version of the paper for publication.

  generalfuture workevidence 5/5
  Keywords: energy model forecasting renewable models sustainability green index integration real time better factor country policy
• DEEP LEARNING ENABLED ELECTRICITY CONSUMPTION PREDICTION SYSTEM FOR HOUSEHOLDS IN OFFA USING SMART METER DATA (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  Based on the outcomes of this study, the following actions are recommended to maximize grid efficiency: 1. Grid Integration for Local Infrastructure: Regional electricity distribution networks operating within the Offa axis should integrate deep learning predictive engines into their centralized distribution management systems to enable real-time peak load forecasting and proactive transformer protection. 2. Data-Driven Dynamic Tariffs: Local energy planning bodies should leverage these predictive insights to design and introduce data-driven, time-of-use pricing structures, encouraging consumers to shift heavy appliance usage away from identified peak periods. 3. Edge Deployment and Scalability: Future implementations should focus on optimizing the deep learning architecture to run on resource-constrained edge computing devices directly inside localized community transformers, allowing for real-time localized inference without overwhelming central communication networks.

  generalrecommendationsevidence 5/5
  Keywords: time grid local distribution networks deep learning predictive real peak driven edge localized based outcomes
• Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data (2024) · Energies · cited 15× · doi

  In a world marked by rapid industrialization and globalization, efficient energy man- agement is playing a key role in smart energy management systems, necessitating the accurate prediction of future energy requirements. In this study, we comprehensively analyzed energy consumption forecasting in an office building in the Houston area, con- sidering the individual consumptions of various categories inside the building (HVAC, plug-ins, etc.) and the overall energy demand. Our focus was on hourly, 3-hourly, quarterly, and daily consumption patterns. To achieve accurate predictions, we trained and evaluated LSTM using a real-world dataset of the building’s electricity consumption. By employing evaluation metrics such as MAPE, MAE, and RMSE, we assessed the performance of our proposed models and obtained promising results. Our results indicate that the models accurately forecast energy usage across various time intervals, including hourly, 3-hourly, quarterly, and daily predictions for individual appliances and total energy consumption. In future work, we plan to expand the dataset size, which will enhance the robustness of our models. Additionally, we aim to explore different regularization techniques to improve the accuracy of predictions. Furthermore, we intend to investigate the effectiveness of deep learning algorithms in long-term energy consumption prediction. We also plan to include additional data, such as relative humidity, wind speed, specific household appliance consumption, and economic factors, to enhance our models’ predictive Energies 2025, 17, 6451 14 of 16 capability. By extending our research in these directions, we aim to contribute to the development of more effective and reliable methods for forecasting energy consumption in buildings. This will provide valuable insights for energy planning and management, facilitating the sustainable and efficient utilization of resources in the face of increasing energy demands. Author Contributions: Conceptualization, M.A. and D.B.; methodology, M.A. and K.K.; software, K.K.; validation, N.N., A.S., and B.Q.; formal analysis, H.T.; investigation, N.N. and A.S.; resources, B.Q.; writing—original draft preparation, M.A. and K.K.; writing—review and editing, all authors; supervision, D.B. and M.A.; project administration, M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript. Funding: This work is supported by the Office of Research and Innovation (IRG project number, 23207) at Alfaisal University, Riyadh, KSA. Data Availability Statement: The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. Acknowledgments: The authors extend their appreciation to the Office of Research and Innovation at Alfaisal University in Riyadh, Saudi Arabia. Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publica- tion of this paper.

  generalfuture workevidence 5/5
  Keywords: energy consumption hourly models authors office building predictions world efficient management accurate prediction future forecasting
• Prediction of Heat Load in Oil and Gas Gathering Stations Based on CNN–LSTM–Attention (2026) · Processes · doi

  Traditional forecasting methods struggle to capture the complex nonlinear relationships and long-term temporal dependencies inherent in heat load data. The lack of a deep learning model that can effectively predict heat load in oil and gas gathering stations. The need for a model that can handle the nonlinear, non-stationary, and long-term temporal dependencies of thermal load data.

  generalstated research gapevidence 5/5
  Keywords: traditional forecasting methods struggle capture complex nonlinear relationships

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