TL;DR
This research confirms that hybrid ML architectures, combined with rigorous feature engineering and hyperparameter optimization, provide a robust and generalizable solution for STLF, and offers substantial benefits for grid reliability, operational planning, and economic efficiency in the power sector.
Accurate short-term load forecasting is essential for efficient smart grid operation and demand-side energy management. Traditional statistical models often struggle to capture nonlinear and irregular residential electricity consumption patterns. This paper proposes a hybrid deep learning architecture combining Tempora...
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-mi...
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...
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.
This study provides an in-depth comparative analysis of four state-of-the-art neural architectures, confirming that high-fidelity point forecasts and rigorously quantified uncertainty can be achieved simultaneously, providing a clear path toward more dependable PV dispatch, reserve allocation, and market participation.
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.
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