The rapid expansion of photovoltaic (PV) systems poses significant challenges to grid stability. Hybrid Energy Systems (HES) are intended to alleviate this volatility, yet their coordinated dispatch often remains suboptimal due to communication delays and ramp-rate constraints. Accurate ultra-short-term PV power forecasting is therefore essential, as it enables preemptive control and timely dispatch adjustments that unlock the full potential of HES. In this study, we propose a novel AI hybrid forecasting framework that integrates a rule-based model with a Decomposition Linear (DLinear) Long Short-Term Memory (LSTM) deep learning core, representing, to the best of our knowledge, a novel integration of a decomposition-based linear model (DLinear) with LSTM networks for ultra-short-term PV power forecasting. The DLinear component decomposes the time series into trend and remainder sequences, which are then independently modeled by separate LSTM networks to capture distinct dynamics. Using data from a 300 kWp PV power station, the framework achieves an average daily prediction accuracy exceeding 93% for both 5-min and 15-min horizons. The model reliably tracks power variations under sunny and rainy conditions, while under volatile cloudy weather its accuracy decreases but still captures essential fluctuation patterns. These results demonstrate the potential of the proposed framework for improving the dispatch and operational reliability of hybrid energy systems. However, further validation across additional seasons and sites is needed to establish broader generalizability.
Reliable forecasting of photovoltaic (PV) and wind power generation, particularly in ultra-short-term and short-term forecasting horizons, constitutes an essential tool for grid stability and the effective management of electric power systems with high renewable energy sources penetration. However, machine learning models trained with standard objective functions such as mean squared error minimization tend to produce smooth forecasting curves and thus fail to predict abrupt power fluctuations, i.e., ramp events, which threaten grid stability. In this paper, a hybrid forecasting framework is proposed that integrates the ramp event detection capability directly into the training process. The main predictor is a Long Short-Term Memory network optimized by a new hybrid algorithm combining advanced Simulated Annealing with Particle Swarm Optimization and trained with a novel combined objective function that aligns correct ramp event detection with high prediction accuracy. The proposed framework is applied to ultra-short-term single-step-ahead wind power forecasting and ultra-short-term multi-step-ahead PV power forecasting, utilizing data from a real-world operating wind turbine and PV park, respectively. The experimental results validate the combined objective function’s efficacy in both case studies, as the proposed forecasting framework achieves the highest ramp event prediction capability, while maintaining relatively low average prediction errors compared to several benchmark models.
S. Kostoglou, Markos A. Kousounadis-Knousen, George J. Tsekouras et al.· IEEE Access· 0 citations
Accurate short-term photovoltaic (PV) power forecasting is increasingly important for maximizing on-site self-consumption and ensuring reliable grid integration as decentralized PV deployment grows. This paper presents a systematic comparison of Long Short-Term Memory (LSTM) and transformer-based architectures for deterministic short-term PV power forecasting using publicly accessible data from multiple climatic regions. The dataset combines multi-year 15-minute PV power measurements from nine plants with corresponding meteorological variables, and it is followed by a unified preprocessing pipeline that includes outlier treatment, interpolation, feature scaling, and correlation-based feature selection. Several feature subsets and input window lengths are evaluated, and Bayesian hyperparameter optimization is employed to refine model configurations for both architectures. The results indicate that using all meteorological variables except cloud coverage with a 2-day input window yields the best performance. Under this configuration, the transformer model outperforms the LSTM model, achieving on average test errors of MSE = 0.0038 and MAE = 0.0265, compared to 0.0055 and 0.0343 for the LSTM, respectively. An analysis of time-resolved residuals shows that both models exhibit the largest errors around noon, while the transformer provides a consistently narrower error distribution over the diurnal cycle. These findings highlight the advantages of attention-based sequence modeling for PV applications and offer practical guidance on feature design, input horizon selection, and hyperparameter ranges for future data-driven PV forecasting studies.
Marcel Lüdecke, Elias Oppermann, Michel Meinert et al.· e+i Elektrotechnik und Infor...· 0 citations
The large-scale integration of photovoltaic (PV) systems into modern power grids impacts operational challenges in reducing the intermittency of solar irradiance. Short-term forecasting, especially with accurate result is essential for grid stability, economic dispatch, and demand-side management. However, the development of robust deep-learning models is frequently constrained by the limited readiness of high-quality and labeled actual generation data. This paper presents a simulation-driven methodology to address this data scarcity problem. A synthetic PV generation dataset is created using a deterministic mathematical model. This method captures the diurnal solar cycle, augmented with Gaussian stochastic noise to simulate the effects of cloud cover and atmospheric turbulence. A Long Short-Term Memory (LSTM) network in two layer is trained on the synthetic dataset and subsequently validated against real hourly PV generation data for Bali, Indonesia. This data was obtained from the PVGIS-ERA5 database. The method performs 1-hour-ahead sequence-to-point forecasting using a 6-hour retrospective window. A persistence baseline model is employed for comparative benchmarking. This experiment found that the LSTM outperforms the persistence baseline on both datasets significantly. For the empirical Bali PV dataset, the LSTM attains a Mean Absolute Error (MAE) and a Root Mean Square Error (RMSE) of 29.75 W and 43.31 W, respectively, with a corresponding R2 value of 0.9474. Compared to the persistence benchmark's MAE of 69.00 W, the network successfully cuts prediction error by 56.9%. These results validate both the simulation-driven training approach and the LSTM's capability for short-term solar forecasting under tropical conditions.
I. D. Saputra, Nicola Schulz, I Nyoman Kusuma Wardana et al.· 2026 International Conferenc...· 0 citations
Reliable short-term photovoltaic (PV) power forecasts are pivotal to grid balancing, intraday market clearing, and asset optimization. This study provides an in-depth comparative analysis of four state-of-the-art neural architectures–a convolutional-recurrent hybrid (CNN-LSTM), an LSTM-Autoencoder, a standalone LSTM, and a time-series Transformer–trained on three years of irradiance and power data with one-minute sampling interval from two meteorologically contrasting PV sites (semi-arid Colorado and desert Nevada). Each model is evaluated in a probabilistic forecasting setting, where kernel density estimation (KDE)-based residual post-processing and quantile extraction are used to obtain calibrated prediction intervals. The benchmark adopts an irradiance-driven forecasting framework, using historical irradiance observations as inputs and future PV power as the prediction target, thereby enabling a controlled assessment of each architecture’s ability to model the underlying irradiance-to-power relationship. Robustness is probed through three stressors: truncated training histories (1–3 years), temporal coarsening (1-, 5-, and 15-minute records), and up to 30% randomly or block-removed observations. Across all tests, CNN-LSTM consistently delivers the narrowest and most reliable intervals, leveraging its convolutional front-end to detect rapid cloud-edge ramps while the LSTM tail preserves long-range context. Compared with a strong LSTM benchmark, the hybrid reduces mean interval width by 22%, lowers the Continuous Ranked Probability Score (CRPS; a scoring rule measuring the distance between the forecast distribution and the observed outcome - lower is better) by 11%, and boosts empirical coverage by 5% (achieving 98.2% reliability). These advantages remain intact under data scarcity, coarse sampling, and substantial missingness, highlighting the critical role of architectures that unite local feature extraction with sequential memory. The findings confirm that high-fidelity point forecasts and rigorously quantified uncertainty can be achieved simultaneously, providing a clear path toward more dependable PV dispatch, reserve allocation, and market participation.
Saloni Dhingra, G. Gruosso, G. Storti Gajani· Neural computing & applicati...· 0 citations
Accurate wind power forecasting is essential for advancing sustainable wind energy utilization, stabilizing new energy power systems, and facilitating the low-carbon and sustainable development of the power industry. Aiming to address the challenges of strong non-stationarity, significant multiscale fluctuations, and the difficulty in jointly modeling local dynamics and long-term dependencies in wind power time series, this study proposes a short-to-medium-term wind power forecasting method based on multiscale decomposition and local-global cooperative modeling. First, Seasonal Trend Decomposition (STL) is employed to extract the trend and seasonal components from the raw time series. The residual series is then subjected to Adaptive Noise-Complemented Empirical Mode Decomposition (CEEMDAN) to reveal the underlying non-stationary characteristics. Based on the dominant frequency characteristics, the decomposed components are reconstructed into high, medium, and low-frequency series, which, together with the trend and seasonal components, form a multidimensional set of input features. Second, to accommodate the time-series characteristics of the multiscale components, we designed an LCNN-Mamba forecasting framework that integrates a Local Convolutional Neural Network (LocalCNN) with a state-space model. The LocalCNN is used to model local and multiscale dynamic features, while the multi-layer Mamba state-space module efficiently captures long-term time-series dependencies, thereby achieving a synergistic enhancement of both local sensitivity and global modeling capabilities. The proposed model was validated using actual operational data from wind farms at two different locations. Experimental results show that the proposed approach outperforms traditional forecasting methods in terms of forecasting accuracy, providing a reliable technical reference for wind power grid-connected operation, power dispatching optimization, and the large-scale and high-quality sustainable development of wind energy resources.
Fan Yang, Jiaying Zhang· Sustainability· 0 citations
Accurate photovoltaic (PV) energy forecasting is vital for grid stability and the global low-carbon transition. However, existing data-driven and channel-independent PV energy forecasting models struggle to capture nonlinear meteorological couplings, heterogeneous physical scales across stations, and high-frequency non-stationary fluctuations. To address these limitations, this study proposes a Physics-Guided Cross-Variable Temporal Transformer architecture. Building upon a channel-independent foundation, we introduce a Cross-Variable Attention mechanism to explicitly reconstruct nonlinear photothermal couplings via dynamic attention weights. To resolve multi-station physical scale discrepancies, a Physical Feature-wise Linear Modulation network utilizes installed capacity as a static prior for adaptive cross-station scale alignment. During optimization, a Time Dynamics-Aware Perceiving Loss jointly penalizes absolute errors and first-order time differences, constraining the network’s tracking ability for transient ramping. Experiments demonstrate that the proposed architecture overcomes traditional channel-isolation limitations. The model achieves a 21.6% reduction in MSE compared to PatchTST, a 44.0% reduction compared to Autoformer, and a 2.7% improvement in R2 over Informer. This provides an accurate, generalizable, and physically interpretable solution for collaborative multi-station distributed PV energy forecasting.
Chen Xie, Mingju Chen, Yuyan Wang et al.· Algorithms· 0 citations