Hybrid Deep Learning Architectures for Improving Short-Term Energy Demand Forecasting

Mariana Silva

Hybrid Deep Learning Architectures for Improving Short-Term Energy Demand Forecasting

Author(s)	Mariana Silva
Country	Portugal
Abstract	Accurate short-term energy demand forecasting is important for electricity generation scheduling, grid stability, demand management, reserve planning, and efficient allocation of power-system resources. Energy demand is influenced by historical consumption patterns, weather conditions, calendar effects, occupancy behavior, economic activities, and unexpected variations in consumer requirements. These interacting influences create nonlinear and time-dependent relationships that may limit the forecasting performance of individual statistical or machine learning models. Hybrid deep learning provides a methodological approach for combining complementary forms of temporal and nonlinear representation within a unified forecasting system. This hypothetical methodological study examines a hybrid deep learning architecture for improving short-term electricity-demand forecasting. The proposed framework integrates temporal feature extraction, recurrent sequence modeling, exogenous weather variables, calendar information, and a final nonlinear prediction layer. An illustrative dataset representing hourly energy-demand observations is divided into hypothetical training, validation, and testing periods. Three conceptual forecasting configurations are compared: a feedforward neural network, a recurrent deep learning model, and a hybrid deep learning architecture. The illustrative analysis evaluates the assumed models using mean absolute error, root mean squared error, and mean absolute percentage error. Additional calculations examine hypothetical forecasting performance across one-hour, six-hour, twelve-hour, and twenty-four-hour prediction horizons. Under the assigned sample values, the hybrid architecture records lower forecasting errors than the two comparison configurations, while forecast uncertainty increases as the prediction horizon becomes longer. These numerical differences are illustrative and are not outputs of implemented learning algorithms. The study concludes that hybrid deep learning offers a useful methodological framework for combining multiple sources of temporal and contextual information in short-term energy forecasting. However, improvements must be demonstrated through reproducible experiments using representative datasets, appropriate benchmark models, temporal validation, and transparent error reporting. The proposed framework therefore provides a conceptual basis for future empirical research rather than evidence of established forecasting superiority.
Keywords	Hybrid Deep Learning, Short-Term Energy Forecasting, Electricity Demand, Neural Networks, Time-Series Forecasting, Recurrent Learning, Load Forecasting, Energy Analytics, Predictive Modeling, Smart Grid.
Field	Engineering
Published In	Volume 1, Issue 2, May-June 2025
Published On	2025-05-30

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Hybrid Deep Learning Architectures for Improving Short-Term Energy Demand Forecasting

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