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Reliable short-term forecasting of solar radiation is crucial to ensuring the smooth operation and optimal scheduling of photovoltaic (PV) power systems. At present, cutting-edge encoder–decoder frameworks that leverage satellite remote sensing imagery are the dominant strategy for accomplishing this task. Yet these approaches reveal clear weaknesses, particularly when using longer prediction windows. Under extended horizons, the resulting maps commonly display blurred spatial patterns and noticeable inaccuracies in radiation levels. Such flaws substantially undermine forecast sharpness and trustworthiness, rendering them inadequate for applications requiring very high accuracy. To address these shortcomings, we introduce GAN-Solar — a novel post-processing architecture for improving short-term solar radiation predictions that leverages Generative Adversarial Networks (GANs). The framework adopts a conditional ED-AttUNet as its generator. A carefully designed discriminator equipped with residual blocks, stepwise resolution reduction, and auxiliary conditional signals is trained to separate genuine from synthetic radiation fields. Through this competitive training — steered by a composite loss and a discriminator acting as an adaptive quality metric — the model progressively sharpens prediction fidelity. Evaluations of real summer solar radiation datasets show that GAN-Solar delivers substantial gains in output quality. Compared with the plain ED-AttUNet baseline, it reduces the Root Mean Square Error by about 3.2% and increases the Structural Similarity Index from 0.84 to 0.87. Overall, the method effectively corrects for texture smearing and intensity warping, yielding noticeably crisper, more faithful solar radiation maps.

A new method for generating time series simulations of electricity price scenarios is introduced. The “Time Series Simulation Conditional Generative Adversarial Network” (TSS-CGAN) produces short-term scenarios of electricity prices. Specifically, the network can output a 24-dimensional vector that reflects the typical dynamics of electricity markets. This approach can serve as an alternative to conventional financial-mathematical techniques, such as statistical factor models, for modeling the price distribution around a given forecast. The dataset spans 3 years from 2020 to 2023. The empirical analysis focuses on the EPEX SPOT market in Europe. An electricity price scenario consists of the prices for the hourly contracts from a day-ahead auction on the EPEX SPOT power exchange. The model takes multivariate time series as input features, including point forecasts of electricity prices along with fundamental data on generation and load profiles. The TSS-CGAN architecture integrates the principles of Conditional Generative Adversarial Networks with 1D Convolutional Neural Networks and Bidirectional Long Short-Term Memory units. Model performance is assessed using both qualitative and quantitative measures. For evaluation purposes, 10,000 simulations are generated for a test period. Qualitative assessment examines whether the model correctly captures and reproduces key electricity market-specific patterns. The quantitative evaluation involves comparing standard error metrics against benchmark models such as DeepAR, Prophet, and Temporal Fusion Transformer, analyzing quantile ranges and error distributions, and conducting a sensitivity analysis. The findings demonstrate that the TSS-CGAN surpasses benchmark models like DeepAR by lowering the continuous ranked probability score by 50% while effectively accounting for market-specific factors, such as the generation of intermittent renewable energies, and responding appropriately to variations in the relevant variables.