The intermittent and volatile characteristics of new energy generation, together with the increasing demand for stable power supply in intelligent industrial systems, make accurate forecasting a critical issue for grid dispatch and electromagnetic energy management. This study systematically reviews the technological evolution of time series analysis methods for wind and photovoltaic (PV) power forecasting and establishes a comparative framework covering classical statistical models, intelligent learning algorithms, and hybrid modeling strategies. Based on two years of operational data collected from an actual wind farm and PV station in East China, the forecasting performance of ARIMA, exponential smoothing, Support Vector Regression (SVR), Long Short-Term Memory (LSTM) networks, and Transformer architectures is comprehensively evaluated, while hybrid approaches based on Empirical Mode Decomposition (EMD) are further investigated. The results demonstrate that model selection should jointly consider forecasting horizon, data characteristics, and computational constraints. Classical statistical methods remain robust under stable operating conditions but are less effective in capturing extreme fluctuations, whereas deep learning approaches exhibit superior capability in modeling long-range temporal dependencies despite reduced interpretability. Decomposition-based hybrid strategies achieve a more balanced performance across diverse scenarios and show enhanced robustness under extreme weather conditions. The study further proposes a structured model selection guideline by matching data characteristics with operational requirements, providing theoretical support for forecasting system design in renewable-energy-driven power networks and offering useful references for electromagnetic energy utilization and intelligent industrial applications.
The study shows that machine learning enables smarter load balancing, better integration of renewable energy, and improved decision-making in power distribution systems, and supports the development of intelligent, sustainable, and data-driven energy.
M. Tarani, Tothadi Sowjanya· International Scientific Jou...· 0 citations
Electricity is a vital resource that powers modern society, and reliable forecasting of electricity demand and supply is essential for the effective operation of power systems. Accurate forecasts allow power system operators to make informed decisions about generation, transmission, and distribution, which can help to prevent blackouts and other disruptions to the electricity supply. Various methods have been developed for forecasting electricity, including statistical methods such as ARIMA and Prophet and artificial intelligence (AI) algorithms such as recurrent neural network (RNN) and support vector machines (SVM). Recent advances in deep learning, particularly Long Short-Term Memory (LSTM) networks, have demonstrated superior performance for time-series forecasting tasks, especially with high-frequency datasets. In this paper, we compared the performance of six methods covering two statistical and four AI algorithms for forecasting electricity demand. They were applied to four different datasets: 1) A monthly KAPSARC, which stands for The King Abdullah Petroleum Studies and Research Center, Dataset in Saudi Arabia with limited historical data, 2) A generated hourly KAPSARC Dataset in Saudi Arabia, 3) An hourly PJM Dataset in USA with a large amount of data, and 4) A generated monthly PJM Dataset in USA. The performance of the approaches was different with each dataset. Overall, the results confirm that data richness particularly hourly granularity is a decisive factor in forecasting accuracy, and that deep learning models require substantial data volumes to outperform statistical baselines. These findings have direct implications for electricity infrastructure planning in Saudi Arabia under Vision 2030.
Kamal M. Othman· Journal of Intelligent Decis...· 0 citations
The empirical results indicate that tree-based ensemble models outperform deep learning approaches under the considered dataset conditions and suggest that ensemble machine learning techniques are more suitable for data-constrained environments, where deep learning models may suffer from overfitting.
Lamiaa Fares, Moad El Kharrim, Mohamed Dakkon· 0 citations
Short-term electricity demand forecasting is a critical enabler of the secure and efficient operation of modern power systems, particularly amid increasing renewable energy integration, smart grid expansion, and the broader energy transition. This paper presents a rigorous comparative analysis of electricity demand forecasting models, encompassing statistical methods, Machine Learning (ML), Deep Learning (DL), and hybrid architectures. A structured taxonomy is proposed to classify models according to their methodological family, application horizon, and data requirements, thereby providing a unified reference framework for researchers and energy-sector practitioners. Models are evaluated using a multi-criteria framework comprising accuracy, robustness, scalability, interpretability, computational cost, and the capacity to handle exogenous variables. The analysis identifies critical research gaps, including the limited integration of probabilistic forecasting into operational contexts and the absence of standardized evaluation protocols under real-world conditions. Future research directions are outlined, with particular emphasis on uncertainty quantification, adaptive learning strategies, and hierarchical forecast coherence in systems with high penetration of distributed energy resources.
A. Torres-Sánchez, Á. Jaramillo-Duque, W. Villa-Acevedo· Processes· 0 citations
Against the backdrop of high-penetration renewable energy integration and increasingly intelligent power communication infrastructures, electricity spot market prices exhibit complex nonlinear fluctuations that directly affect the operational costs and scheduling efficiency of energy-intensive industrial systems. Accurate price forecasting is therefore essential for coordinated energy management and reliable information transmission in modern smart grids. This paper proposes a time-series prediction algorithm integrating fractal analysis and deep learning to capture both long-term memory characteristics and local abrupt variations in electricity price sequences. The method first quantifies long-range correlations using the Hurst exponent derived from rescaled range analysis, then extracts multi-scale fluctuation features through multifractal detrended fluctuation analysis, and finally embeds fractal characteristics into the loss function of a Long Short-Term Memory (LSTM) network to construct a fractal-aware prediction model. A dualwindow strategy is adopted to reconcile the statistical requirements of fractal estimation with temporal responsiveness. Experimental results based on actual clearing data from five domestic spot market pilot regions demonstrate that the proposed model consistently outperforms conventional ARIMA, standard LSTM, VMD-LSTM, CEEMDAN-BERT-LSTM, and Transformer-based methods in both MAPE and RMSE. The proposed framework provides an effective decisionsupport tool for intelligent energy management and offers potential reference value for signal-aware forecasting and information processing in advanced electromagnetic and communication-enabled power systems.
M. Song, C. Su, G. Gao et al.· Advanced Electromagnetics· 0 citations
The successful forecast of the solar photovoltaic (PV) power processing is essential in increasing grid stability, and in the ultimate inclusion of renewable energy. This paper is a research project aiming to introduce a hybrid classifier of Random Forest (RF), K-Nearest Neighbors (KNN), and Elastic Net (EN) to enhance multi-regional solar PV power forecasting using time-series. Preprocessing of Hourly PV generation data was done by taking out the temporal feature like hour, day and month to improve forecasting. To compare the proposed hybrid model with individual algorithms, the coefficient of determination (R2), mean absolute error (MAE) were used to evaluate the model. The results of the experiment indicate that the hybrid model had an R 2 of 0.91, which was higher than RF (0.88), KNN (0.82), and EN (0.79), and nearly halved the MAE, relative to single models. The results prove that the combination of multiple regression methods would lead to better prediction robustness, as well as lower variance and increase forecasting accuracy, which makes the suggested method an appropriate tool to study smart grids and sustainable energy planning.
S. R., S. Rubavathy· 2026 11th International Conf...· 0 citations
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