Evaluating the Integration of Fuzzy and Non-Fuzzy Clustering Approaches into LSTM for the Power Consumption Forecasting Utilizing the Case Study Dataset of Tetuan City
Received: 6 May 2025 | Revised: 2 July 2025 and 10 July 2025 | Accepted: 11 July 2025 | Online: 26 August 2025
Corresponding author: Etna Vianita
Abstract
This study explores the integration of fuzzy and non-fuzzy clustering techniques into Long Short-Term Memory (LSTM) networks for short-term electricity consumption forecasting. Using a high-resolution dataset from Tetuan City, Morocco, three LSTM-based configurations were evaluated to assess the effects of contextual clustering on the model accuracy. While the proposed LSTM with K-Means model yielded a slightly higher Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) compared to a prior Multilayer Perceptron (MLP) with Fuzzy C-Means (FCM) baseline, it achieved a superior coefficient of determination (R²) of 0.9978, indicating enhanced variance explanation. The findings suggest that incorporating non-fuzzy clustering into deep temporal models offers a practical alternative to fuzzy-based approaches, particularly in scenarios where the long-term sequence patterns are critical.
Keywords:
LSTM, power consumption forecasting, time series, clustering, Fuzzy C-Means (FCM), K-MeansDownloads
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Copyright (c) 2025 Eko Adi Sarwoko, Etna Vianita, Adi Wibowo
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