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Transformer-Based Deep Learning Model for Short-Term and Long-Term Energy Demand Forecasting in Smart Cities.

Najaad OubeBlika
Volume 5

Abstract

Accurate energy demand forecasting is a critical requirement for efficient smart city management, sustainable urban planning, and reliable operation of modern power systems. As cities become increasingly dependent on digital infrastructure, electric transportation, distributed renewable energy resources, smart buildings, and Internet of Things-based monitoring systems, energy consumption patterns are becoming more complex, dynamic, and nonlinear. Short-term demand forecasting is essential for real-time grid operation, demand response, energy dispatch, peak-load management, and electricity market participation, while long-term forecasting supports infrastructure planning, capacity expansion, renewable integration, and strategic policy development. Traditional statistical forecasting methods and conventional machine learning models often struggle to capture long-range temporal dependencies, multi-scale seasonality, nonlinear consumption behavior, and the influence of heterogeneous urban factors. To address these challenges, this paper proposes a transformer-based deep learning model for short-term and long-term energy demand forecasting in smart cities.

The proposed framework leverages the self-attention mechanism of transformer architectures to model complex temporal relationships across multiple forecasting horizons. Unlike recurrent neural networks, which process sequences sequentially and may suffer from limited long-term memory, transformers can capture dependencies between distant time steps more effectively through parallel attention-based learning. The model integrates historical electricity consumption, weather variables, calendar information, occupancy patterns, socio-economic indicators, electricity price signals, renewable generation profiles, transportation activity, and smart meter measurements. A comprehensive preprocessing pipeline is applied to handle missing values, remove outliers, normalize continuous variables, encode temporal features, align multi-source urban datasets, and construct multi-resolution input sequences suitable for both short-term and long-term forecasting.

The proposed architecture incorporates positional encoding, multi-head self-attention, temporal convolutional embedding, feed-forward layers, and horizon-specific output modules to generate accurate energy demand predictions at hourly, daily, weekly, monthly, and seasonal scales. For short-term forecasting, the model focuses on high-resolution consumption fluctuations driven by weather changes, occupancy behavior, electric vehicle charging, and peak-hour demand. For long-term forecasting, the model captures structural trends related to population growth, urban expansion, seasonal variation, economic activity, policy changes, and renewable energy adoption. To improve robustness, the framework incorporates probabilistic forecasting and uncertainty estimation, enabling grid operators and city planners to quantify prediction confidence and prepare for demand variability.

The proposed model can be evaluated using smart meter datasets, city-scale electricity consumption records, building energy datasets, weather archives, and publicly available energy benchmarking datasets. Performance is assessed using Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, symmetric MAPE, normalized RMSE, coefficient of determination, prediction interval coverage probability, and computational efficiency. Comparative analysis is conducted against persistence models, ARIMA, Prophet, support vector regression, random forest, XGBoost, LSTM, GRU, temporal convolutional networks, and conventional encoder–decoder models. Experimental evaluation is expected to demonstrate that the transformer-based model achieves superior forecasting accuracy across both short-term and long-term horizons by effectively capturing multi-scale temporal dependencies and heterogeneous urban energy consumption patterns.

For smart city applications, the forecasting outputs are integrated into decision-support modules for load balancing, demand response scheduling, distributed energy resource coordination, battery storage management, grid congestion prevention, and urban energy policy planning. The model also supports interpretability through attention visualization, feature attribution analysis, and temporal importance mapping, allowing operators to identify the weather conditions, time periods, consumption sectors, and urban variables that contribute most strongly to predicted energy demand. This transparency improves trust and facilitates practical deployment in city energy management platforms.

The proposed transformer-based framework offers a scalable, accurate, and interpretable solution for energy demand forecasting in smart cities. By supporting both operational short-term forecasting and strategic long-term planning, the system can enhance grid reliability, reduce energy waste, improve renewable energy integration, optimize infrastructure investment, and contribute to more sustainable and resilient urban energy systems.

Keywords

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