A new hierarchical forecasting structure denoted as INRBO-SSA-LSTM, which significantly outperforms traditional baseline models across all indicators and effectively accommodates temporal demand variations, offering a robust foundation for the advancement of intelligent power management technology.
Abstract
Accurate power load forecasting is critical for the efficient operation of industrial microgrids. However, raw meteorological and consumption data typically exhibit non-stationary characteristics, complicating the hyperparameter tuning of deep learning models, and subsequently degrading the prediction accuracy of these frameworks. To address the aforementioned challenges, a new hierarchical forecasting structure denoted as INRBO-SSA-LSTM is proposed in this paper. First, Pearson correlation analysis is employed for feature reduction, identifying the four main factors to mitigate the dimensionality curse. Building upon this foundation, a refined Newton-Raphson-Based Optimizer (INRBO) is introduced, integrating a cosine adaptive t-distribution perturbation, a boundary-aware non-uniform steering scheme, and a fitness-aware hybrid perturbation mechanism. Evaluated against the CEC2022 benchmark suite, comprehensive evaluations reveal that the INRBO demonstrates superior global exploration and local refinement capabilities compared to baseline algorithms when assessed on the CEC2022 benchmark suite for foundational optimization performance. Furthermore, rigorous testing on the CEC2017 suite across 10, 30, and 50 dimensions successfully validates its exceptional robustness and search capabilities in high-dimensional spaces. INRBO functions as a dual-stage optimizer within the proposed framework; in the initial phase, it dynamically calibrates the parameters of Singular Spectrum Analysis (SSA) to extract deterministic load patterns, achieving a maximum signal-to-noise ratio of 15.87 dB; in the second phase, it optimizes the global hyperparameters of the Long Short-Term Memory (LSTM) network. Validated using actual industrial microgrid data in Jiangsu Province, China, the proposed method significantly outperforms traditional baseline models across all indicators; specifically, the prediction error (RMSE = 10.9764, MAPE = 3.7866%) is substantially minimized, and the coefficient of determination (R2 = 0.9741) is highly optimal. This adaptable framework effectively accommodates temporal demand variations, offering a robust foundation for the advancement of intelligent power management technology.
For smart grid power system planning and operation, short-term load forecasting is crucial. Important decisions including determining system safety, scheduling fuel, economically dispatching electricity, and selling energy can be aided by accurate day-ahead estimates. However, due to its reliance on external variables like weather, the process is intricate and computationally intensive. In order to address this issue, the paper proposes an LSTGR-based architecture that systematically enhances STLF in Smart Grids. The input characteristics are first scaled correctly using data normalisation. A hybrid feature selection method combining XGB and RF is utilised to determine the most essential features. Afterwards, RFE is employed to eliminate superfluous attributes. To facilitate learning, a hybrid deep learning model is trained using the updated dataset. This model combines LSTM and GRU. Using assessment criteria such as MAPE, MAE, MSE, and RMSE, the results demonstrate that the LSTGR model outperforms other models. With an RMSE of only 1.8%, the model clearly excels at producing accurate predictions. All things considered, the model successfully improves the reliability of forecasts while being computationally efficient. Because of this, it is an excellent option for smart grid applications in the actual world.
L. Jayavani, Banoth Ashwini, Kolkur Swabhavika et al.· 2026 7th International Confe...· 0 citations
This study compares two multi-step forecasting strategies for 24-h horizons: a direct 24→24 strategy and a recursive strategy based on sequential 24→1 predictions, and concludes that the selection of a forecasting strategy depends on the required balance between overall accuracy, temporal stability, and computational cost.
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Overall, the study demonstrates that recurrent deep learning architectures provide reliable and operationally relevant solutions for electricity consumption forecasting in highly structured energy demand series.
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Accurate wind power forecasting is critical for grid stability and long-term sustainability, but mountainous wind farms face challenges from complex micro-meteorology, restricted communication, and non-IID data, exacerbated by data silos that prevent centralized learning. Most federated learning relies on data-driven averaging that ignores multi-farm coupling, or adopt complex local models that increase communication overhead. To address these, a physics-guided personalized federated approach is proposed to enhance wind power forecasting. Its core is a physics-guided aggregation mechanism that constructs a dynamic weight matrix from distance, elevation, and real-time wind direction to enable personalized aggregation capturing multi-farm coupling. The federated framework combines a shared CNN-LSTM with multi-head attention for regional patterns and a personalized layer for local microclimate. A risk-aware asymmetric loss is incorporated to penalize high-power errors, enhancing operational reliability under high-power conditions. Validation on mountainous wind farms for 3-day forecasting under typical and extreme scenarios across wet, dry, and normal seasons shows that the average R2 exceeds 0.95, and the average RMSE is reduced by more than 24% compared to baselines, achieving high accuracy under strict privacy preservation. By enabling multi-farm coupling under data isolation, this approach achieves high forecasting accuracy on the studied wind farms, showing promise for similar ones.
A framework for a comparative evaluation of four representative forecasting methods: the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, Extreme Gradient Boosting, the Long Short-Term Memory (LSTM) neural network, and a hybrid Variational Mode Decomposition–LSTM (VMD-LSTM) model is proposed.
Pratiman Patel, Prajwal Pal· International Journal for Re...· 0 citations
Accurate short-term load forecasting is important for running power systems efficiently and managing smart grids. In this study, we present an improved Temporal Convolutional Network (TCN) model and compare eight optimization algorithms: Adam, AdaBelief, RAdam, Ranger, AdamP, NovoGrad, Adan, and SAM. We used hourly electricity load data from Denmark to test each optimizer under the same settings and with three different random seeds to ensure a fair and reliable comparison. All optimizers showed strong predictive accuracy, with root mean square error (RMSE) values between 0.03 and 0.05. Adan and AdamP had the lowest errors and were the most stable. These results show that the choice of optimizer has a big impact on how well TCN models learn and generalize. Our framework offers a solid benchmark for building adaptive and reliable forecasting systems for future smart grids.
Hmeda Musbah, Abdussalam Mohamed, Hamed H. Aly· 2026 IEEE Canadian Atlantic...· 0 citations
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