The Council of Experts: A Heterogeneous Rational-Aware Framework for Robust Energy Forecasting

Intelligent Energy Forecasting Framework Using Deep Neural Networks for Renewable Power Systems

Sound prediction of existence of future generation based on stochastic sources is a critical issue because of high nonlinearities, high transitions in the environment, and the nature of inherent uncertainty in observational information. The paper proposes an integrated architecture of deep learning, which is the Hierarchical Regime-Adaptive Probabilistic Network (HRAPN) that aims to overcome these constraints using an end-to-end learning framework. The solution selection boasts of hierarchical representation induction with a latent regime adaptation mechanism that modulates dynamically the internal model behavior in a non-stationary environment. Besides that, attention guided dependency synthesis module performs informative temporal context aggregation selectively to allow an efficient long horizon modeling without impaired performance. In contrast to the more traditional deterministic approaches, HRAPN uses probabilistic model of output in order to explicitly model predictive uncertainty, which enhances robustness and reliability of the estimated decisions. The system takes directly heterogeneous and multivariate data, without explicit features or domain pre-treatment. It is experimentally assessed that the presented method achieves higher results as compared to existing baselines in accuracy, stability, and uncertainty calibration in various forecast periods. The findings validate the performance of regime perceiving, hierarchical abstraction and probabilistic inference in the same learning process. The proposed HRAPN model offers a scaffoldable and adaptable evaluation of the dynamic generation modeling under both variable operating conditions with data. The suggested framework attains an overall accuracy of 96.6%, illustrating its robust prediction reliability and exceptional performance relative to current methodologies.

An INRBO-SSA-LSTM Hybrid Framework for Short-Term Power Load Forecasting in Smart Microgrids

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.

Short-Term Load Forecasting for Residential-Level Smart Microgrids: A Comparative Evaluation of Machine Learning and Deep Learning Architectures

Accurate short-term load forecasting (STLF) is essential for modern grid operations, enabling efficient scheduling, demand response, and renewable energy integration. This paper presents a systematic comparison of five forecasting architectures applied to a large dataset of 98 residential homes, with 1-minute and 15-minute smart meter readings spanning 2015-2023. The models include an XGBoost pipeline with extensive feature engineering, a tuned CatBoost implementation, a feedforward neural network with multi-output regression, a multi-scale convolutional Kolmogorov-Arnold network (MCKAN), and a long short-term memory (LSTM) network with a 7-day lookback. All models are trained globally, pooling data across homes while incorporating home-specific categorical variables, including an assignment to a simulated microgrid topology with nine kiosks and three phases. Hyperparameter optimization is performed using Optuna and Keras Tuner. CatBoost achieves the lowest test MAE across all horizons, from 0.4089 (15-minute) to 0.7419 (30-day), outperforming XGBoost by 4-10% and deep learning models by larger margins. The findings support Sustainable Development Goals 7, 9, and 13 and provide actionable insights for energy management, particularly in the South African context of load shedding and grid decarbonization.

Evaluating Time-Series Foundation Models for Cooling Demand Forecasting with Little Data

Physics-Informed Deep Learning and Rule-Based adjustment PIDL-RB for online Building Energy Management System: A Comparative Study

REINFORCEMENT LEARNING WITH LOAD FORECASTING FOR SMART HOME ENERGY MANAGEMENT

This paper proposes H-UPF (Hybrid Universal Policy with Forecasting), a hybrid intelligent framework for scalable sequential decision-making in heterogeneous environments under uncertainty. The architecture integrates probabilistic multi-horizon forecasting via a Temporal Fusion Transformer with continuous control via Proximal Policy Optimization, embedding predictive quantile distributions directly into the agent’s state representation. A Dynamic Adaptation Layer normalizes observations relative to instance-specific scales, enabling zero-shot policy transfer across environments with 18.5× variability in operating characteristics — without inter-agent communication or per-instance retraining. Validated on two real-world residential energy management datasets (REFIT: 20 UK households; CityLearn: 6 US buildings with real PV profiles), the framework achieves 88.4% of the theoretical optimum in zero-shot transfer, outperforming meta-learning (MAML-PPO) by 8.4 percentage points (Wilcoxon p = 0.003, Cohen’s d = 1.42). Ablation analysis identifies the adaptation layer as the dominant contributor (−16.2 p.p. upon removal), while probabilistic forecasting adds +6.8 p.p. through proactive scheduling. The learned policy is robust to reward parameter variations (≤3.2 p.p. sensitivity across 5× range) and supports practical deployment: 9.8 h one-time training, 18.4 ms inference per control step.