To explore the application of the proposed framework

The gap

Evidence profile

Stated in the cells future research and cells research gap sections of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 4 representative gaps

• Enhancing Solar Power Forecasting Accuracy Using HMPCS and Machine Learning Techniques: An Applied Study (2026) · F1000Research · doi

  To explore the application of the HMPCS-ML framework to other renewable energy sources. - To investigate the use of other metaheuristics and machine learning models in the HMPCS-ML framework. - To develop a multi-objective optimization approach for solar power forecasting.

  generalstated in cells future researchevidence 5/5
  Keywords: explore application hmpcs-ml framework other renewable energy sources
• An Optimized Machine Learning Framework for Smart Grid Energy Management (2026) · International Journal of Advanced Research in Science Communication and Technology · doi

  There is a need for intelligent and optimized decision strategies in smart grid energy management. - There is a lack of comprehensive evaluations of machine learning frameworks for smart grid energy management. - There is a need for further research on the application of machine learning techniques in smart grid energy management.

  generalstated in cells research gapevidence 5/5
  Keywords: there need intelligent optimized decision strategies smart grid
• An intelligent SCADA-integrated deep learning framework for bird-safe offshore wind farm operation (2026) · Scientific Reports · doi

  To explore the application of the proposed framework to various offshore wind farms. - To investigate the potential of integrating other machine learning techniques with the proposed framework. - To evaluate the performance of the proposed model in real-world scenarios.

  generalstated in cells future researchevidence 5/5
  Keywords: explore application proposed framework various offshore wind farms
• Rule-Based vs. Machine Learning Anomaly Detection for Off-Grid Renewable Energy IoT Telemetry: A Comparative Benchmark with Adaptive Thresholding, Cyber-Physical Discrimination, and Hardware-Algorithmic Co-Design (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The development of more accurate and reliable anomaly detection systems for off-grid renewable energy IoT systems. - The evaluation of the proposed approach in real-world scenarios. - The exploration of other machine learning techniques and features for anomaly detection.

  generalstated in cells future researchevidence 5/5
  Keywords: development accurate reliable anomaly detection systems off-grid renewable

Questions about this gap