Machine learning approaches for resource management and forecasting in energy consumption systems

Renewable Energy Systems (RES) have become necessary with the growing need for sustainable energy globally; however, RES faces several problems such as power outages, instability and economic loss. These challenges are structured into three specific technical hurdles: (i) intermittency of wind and solar energy, (ii) forecasting uncertainty, and (iii) grid integration issues. In this context, recent technological trends in short-term wind power generation forecasting have increasingly moved towards hybridizing deep learning models with optimization algorithms to mitigate resource uncertainty. This study finds that Artificial Intelligence (AI) is one of the best solutions for overcoming these problems. To provide conceptual clarity, this research distinguishes AI as the overarching intelligent framework, while Machine Learning (ML) enables algorithmic learning of relationships from data, and Deep Learning (DL) utilizes hierarchical architectures for capturing complex non-linear temporal patterns. Methods such as Support Vector Machines (SVM) and Random Forest (RF) are applied alongside DL for predicting the amount of energy produced from RES and scheduling maintenance activities. To address existing research gaps, this research presents an innovative approach through combining a unique four-stage scientific data cleaning method with a two-phase AI stabilization system. The system uses the Markowitz Model as the first stage of determining the optimal hybrid ratio of wind and solar resources in addition to employing an energy-constrained battery smoothing process to reduce residual fluctuations. Results indicate that AI-based predictive maintenance can decrease the operational expenditure (OPEX) of RES up to 30.0%. Furthermore, the Long Short-Term Memory (LSTM) model demonstrated superior predictive power, achieving a Coefficient of Determination (R²) of up to 0.988, whereas a two-stage AI framework integrating Markowitz-based portfolio optimization and energy-constrained battery control achieves an average variance reduction of 95.4% in grid stability.