One of the main challenges in energy resource management is accurately predicting demand to reduce production and storage costs. The time-series behavior of gas demand is highly complex and nonlinear due to the influence of variables such as weather conditions, prices, and population. In this study, a hybrid model based on Long Short-Term Memory (LSTM) neural networks, combined with the Variational Mode Decomposition (VMD) algorithm and the Cosine-Sine Algorithm (SCA), is proposed. The VMD algorithm decomposes time-series data and extracts the main components, while the SCA optimizes the parameters of the LSTM network to improve prediction accuracy. The prediction accuracy of the model is evaluated using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), and its performance is compared with three benchmark models: the simple LSTM neural network, the Support Vector Regression (SVR) model, and the AutoRegressive Integrated Moving Average (ARIMA) model. The results show that the proposed model provides higher accuracy than other models and can be used as an efficient tool for energy resource planning and management.
Salary, M. , Mansouri, N. , Mohammad Hasani Zade, B. and Hosseini, M. M. (2025). Intelligent Forecasting of Urban Gas Consumption Using LSTM Neural Network and Weather Variables. Energy Engineering and Management, 15(1), -. doi: 10.22052/eem.2025.256659.1111
MLA
Salary, M. , , Mansouri, N. , , Mohammad Hasani Zade, B. , and Hosseini, M. M. . "Intelligent Forecasting of Urban Gas Consumption Using LSTM Neural Network and Weather Variables", Energy Engineering and Management, 15, 1, 2025, -. doi: 10.22052/eem.2025.256659.1111
HARVARD
Salary, M., Mansouri, N., Mohammad Hasani Zade, B., Hosseini, M. M. (2025). 'Intelligent Forecasting of Urban Gas Consumption Using LSTM Neural Network and Weather Variables', Energy Engineering and Management, 15(1), pp. -. doi: 10.22052/eem.2025.256659.1111
CHICAGO
M. Salary , N. Mansouri , B. Mohammad Hasani Zade and M. M. Hosseini, "Intelligent Forecasting of Urban Gas Consumption Using LSTM Neural Network and Weather Variables," Energy Engineering and Management, 15 1 (2025): -, doi: 10.22052/eem.2025.256659.1111
VANCOUVER
Salary, M., Mansouri, N., Mohammad Hasani Zade, B., Hosseini, M. M. Intelligent Forecasting of Urban Gas Consumption Using LSTM Neural Network and Weather Variables. Energy Engineering and Management, 2025; 15(1): -. doi: 10.22052/eem.2025.256659.1111
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