OPTIMIZATION OF HYBRID TCN, LSTM, AND LIGHTGBM MODELS FOR ENERGY CONSUMPTION FORECASTING IN SMART HOMES

Accurate short-term load forecasting is a key task for effective energy resource management in smart home systems. Hybrid models that combine deep learning (DL) architectures and decision tree ensembles are a leading direction in modern research. An analysis of recent publications confirms that comparing Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) networks is a popular topic, and hybridization with LightGBM and the use of error correction strategies ("residual forecasting") are proven practices for improving accuracy. However, a literature review reveals several unresolved parts of the general problem: 1) the lack of a systematic analysis of the trade-off between forecast accuracy and computational cost (training time, resource requirements), which is critical for implementation on Internet of Things (IoT) devices; 2) insufficient research on the impact of feature engineering, particularly feature selection, on the computational efficiency of hybrid models; 3) a tendency to focus on accuracy metrics without providing practical methodologies for selecting the optimal model depending on the specific task. This work aims to fill these gaps. A multi-stage exp