TL;DR
The experimental results show that the model provides consistent improvements in forecasting accuracy across the tested configurations, and suggest that combining signal decomposition with hybrid deep learning architectures can be beneficial for temperature forecasting in the considered settings.
Air temperature is a critical meteorological parameter that dynamically affects density altitude and Maximum Take-Off Weight (MTOW) calculations in aviation operations. Although the 10-minute resolution Automatic Weather Station (AWS) data at Soekarno-Hatta International Airport effectively captures micrometeorological...
The proposed CNN–BiLSTM–Attention framework performed better than conventional benchmark models of machine learning and deep learning, achieving a Root Mean Square Error of 0.088 and Seasonal and atmospheric-condition evaluations also showed consistent performance across different seasonal and atmospheric conditions.
The obtained results indicate that integrating signal decomposition with attention-based bidirectional learning can provide complementary benefits for forecasting under highly non-stationary urban conditions, particularly at shorter forecasting horizons.
Variational mode decomposition (VMD) separates heating, ventilation, and air-conditioning (HVAC) load into narrower-band signals, but the number of modes and the bandwidth-penalty parameter must be selected without using test-set outcomes. We evaluated a diagnostic-guided selection procedure using a multichannel tempor...
Reliable short-term prediction of global horizontal irradiance (GHI) is essential for maximizing photovoltaic (PV) power generation, improving grid reliability, and enabling efficient integration of renewable energy sources. This study proposes Lite-BiAttn, a lightweight hybrid deep learning framework that combines one...
Hydroclimatic extremes are projected to intensify across monsoon-dependent river basins, yet reliable forecasting remains limited by three persistent gaps: deep learning models rarely integrate spatial and temporal processing in a unified pipeline, almost always target a single variable, and offer limited interpretab...
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