<ul><li>This study proposes a framework for long-term electricity demand prediction based solely on historical consumption data.</li><li>The method combines Non-negative Tensor Factorization (NTF) and a Genetic Algorithm to optimize the hyperparameters of time series models.</li><li>Experiments using real-world electricity data from Japan show that the proposed method achieves lower mean squared error than baseline approaches.</li><li>The framework offers an interpretable, flexible, and scalable approach to long-term electricity demand prediction.</li></ul>

Long-Term Electricity Demand Prediction Using Non-negative Tensor Factorization and Genetic Algorithm-Driven Temporal Modeling

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