This paper addresses the challenges in forecasting electrical energy consumption in the era of renewable energy integration. It reviews advanced adaptive forecasting methodologies and analyzes the evolution of research through bibliometric analysis. The review highlights key contributions and limitations of current models, emphasizing the challenges of traditional methods. Long Short-Term Memory (LSTM) networks, optimization techniques, and deep learning are identified as potential solutions for modeling the dynamic nature of energy consumption, but they also come with higher computational demands and data requirements. The paper aims to provide a balanced view of current advancements and challenges in forecasting methods, guiding researchers, policymakers, and industry experts. It advocates for collaborative innovation in adaptive methodologies to enhance forecasting accuracy and support the development of resilient, sustainable energy systems. - **Adaptive Forecasting in Energy Consumption**: Challenges and Advanced Methodologies - **Bibliometric Analysis**: Evolution of Research and Key Contributions - **Methodology**: Detailed Examination of Selected Papers - **State of the Art**: Analysis of Influential Papers - **Discussion and Analysis**: Key Themes, Challenges, and Future Directions - **Conclusions**: Insights and Recommendations for Future Research - **Introduction**: Overview of Adaptive Forecasting in Energy and Its Significance - **Bibliometric Analysis**: Comprehensive Review of High-Impact Publications - **Methodology**: Detailed Explanation of Bibliometric Techniques - **State of the Art**: Analysis of Influential Papers - **Discussion and Analysis**: Key Themes, Challenges, and Future Directions - **Conclusions**: Insights and Recommendations for Future Research - **Deep Learning Approaches**: Applications in Energy Consumption, Renewable Energy, Environmental Forecasting, and Economic Forecasting - **Renewable Energy Approaches**: Innovations in Solar Irradiance, Microgrid Technologies, and Renewable Energy Forecasting - **Environmental and Agricultural Applications**: Integration of Advanced Technologies in Agriculture and Environmental Forecasting - **Functional Data Analysis**: Advanced Models for Electricity Demand and Price Forecasting - **Economic and Price Forecasting**: Techniques for Predicting Market Trends and Economic Indicators - **Advanced Methodologies and Comparisons**: Comparative Studies and Innovations in Forecasting Techniques - **Other Approaches**: Hybrid Models and Specialized Applications - **Electricity Demand Forecasting**: Ensuring efficient energy distribution and supporting renewable energy integration. - **Deep Learning and Neural Networks**: Revolutionizing data forecasting with advanced models. - **Machine Learning in Forecasting**: Enhancing prediction accuracy through adaptive algorithms. - ** Renewable Energy and Forecasting**: Addressing the challenges of variable renewable energy sources. - **Artificial Intelligence in Forecasting**: Improving prediction capabilities with vast datasets and advanced algorithms. - **Time Series Prediction**: Classic statistical method for future value prediction. - **Optimization Algorithms in Forecasting**: Refining models for better accuracy. - **Environmental and Climate Considerations**: Forecasting climatic patterns for mitigation and adaptation strategies. - **Economic Factors in Forecasting**: Predicting market trends andThis paper addresses the challenges in forecasting electrical energy consumption in the era of renewable energy integration. It reviews advanced adaptive forecasting methodologies and analyzes the evolution of research through bibliometric analysis. The review highlights key contributions and limitations of current models, emphasizing the challenges of traditional methods. Long Short-Term Memory (LSTM) networks, optimization techniques, and deep learning are identified as potential solutions for modeling the dynamic nature of energy consumption, but they also come with higher computational demands and data requirements. The paper aims to provide a balanced view of current advancements and challenges in forecasting methods, guiding researchers, policymakers, and industry experts. It advocates for collaborative innovation in adaptive methodologies to enhance forecasting accuracy and support the development of resilient, sustainable energy systems. - **Adaptive Forecasting in Energy Consumption**: Challenges and Advanced Methodologies - **Bibliometric Analysis**: Evolution of Research and Key Contributions - **Methodology**: Detailed Examination of Selected Papers - **State of the Art**: Analysis of Influential Papers - **Discussion and Analysis**: Key Themes, Challenges, and Future Directions - **Conclusions**: Insights and Recommendations for Future Research - **Introduction**: Overview of Adaptive Forecasting in Energy and Its Significance - **Bibliometric Analysis**: Comprehensive Review of High-Impact Publications - **Methodology**: Detailed Explanation of Bibliometric Techniques - **State of the Art**: Analysis of Influential Papers - **Discussion and Analysis**: Key Themes, Challenges, and Future Directions - **Conclusions**: Insights and Recommendations for Future Research - **Deep Learning Approaches**: Applications in Energy Consumption, Renewable Energy, Environmental Forecasting, and Economic Forecasting - **Renewable Energy Approaches**: Innovations in Solar Irradiance, Microgrid Technologies, and Renewable Energy Forecasting - **Environmental and Agricultural Applications**: Integration of Advanced Technologies in Agriculture and Environmental Forecasting - **Functional Data Analysis**: Advanced Models for Electricity Demand and Price Forecasting - **Economic and Price Forecasting**: Techniques for Predicting Market Trends and Economic Indicators - **Advanced Methodologies and Comparisons**: Comparative Studies and Innovations in Forecasting Techniques - **Other Approaches**: Hybrid Models and Specialized Applications - **Electricity Demand Forecasting**: Ensuring efficient energy distribution and supporting renewable energy integration. - **Deep Learning and Neural Networks**: Revolutionizing data forecasting with advanced models. - **Machine Learning in Forecasting**: Enhancing prediction accuracy through adaptive algorithms. - ** Renewable Energy and Forecasting**: Addressing the challenges of variable renewable energy sources. - **Artificial Intelligence in Forecasting**: Improving prediction capabilities with vast datasets and advanced algorithms. - **Time Series Prediction**: Classic statistical method for future value prediction. - **Optimization Algorithms in Forecasting**: Refining models for better accuracy. - **Environmental and Climate Considerations**: Forecasting climatic patterns for mitigation and adaptation strategies. - **Economic Factors in Forecasting**: Predicting market trends and