Weather forecasts from numerical weather prediction models play a central
role in solar energy forecasting, where a cascade of physics-based models is
used in a model chain approach to convert forecasts of solar irradiance to
solar power production, using additional weather variables as auxiliary
information. Ensemble weather forecasts aim to quantify uncertainty in the
future development of the weather, and can be used to propagate this
uncertainty through the model chain to generate probabilistic solar energy
predictions. However, ensemble prediction systems are known to exhibit
systematic errors, and thus require post-processing to obtain accurate and
reliable probabilistic forecasts. The overarching aim of our study is to
systematically evaluate different strategies to apply post-processing methods
in model chain approaches: Not applying any post-processing at all;
post-processing only the irradiance predictions before the conversion;
post-processing only the solar power predictions obtained from the model chain;
or applying post-processing in both steps. In a case study based on a benchmark
dataset for the Jacumba solar plant in the U.S., we develop statistical and
machine learning methods for post-processing ensemble predictions of global
horizontal irradiance and solar power generation. Further, we propose a neural
network-based model for direct solar power forecasting that bypasses the model
chain. Our results indicate that post-processing substantially improves the
solar power generation forecasts, in particular when post-processing is applied
to the power predictions. The machine learning methods for post-processing
yield slightly better probabilistic forecasts, and the direct forecasting
approach performs comparable to the post-processing strategies.

通过评估后处理方法在模型链方法中的应用策略，我们的研究旨在系统地评估不应用任何后处理，仅在转换之前对辐照度预测进行后处理，仅对模型链获得的太阳能预测进行后处理或在两个步骤中应用后处理等方法，并且我们提出了一种基于神经网络的直接太阳能预测模型。结果表明，后处理显著改善了太阳能发电预测，尤其是在电力预测中应用后处理。机器学习方法稍微提供了更好的概率预测，而直接预测方法与后处理策略相当。

通过后处理和机器学习改善概率太阳能预测的模型链方法

Improving Model Chain Approaches for Probabilistic Solar Energy  Forecasting through Post-processing and Machine Learning

The prediction of solar power generation is a challenging task due to its
dependence on climatic characteristics that exhibit spatial and temporal
variability. The performance of a prediction model may vary across different
places due to changes in data distribution, resulting in a model that works
well in one region but not in others. Furthermore, as a consequence of global
warming, there is a notable acceleration in the alteration of weather patterns
on an annual basis. This phenomenon introduces the potential for diminished
efficacy of existing models, even within the same geographical region, as time
progresses. In this paper, a domain adaptive deep learning-based framework is
proposed to estimate solar power generation using weather features that can
solve the aforementioned challenges. A feed-forward deep convolutional network
model is trained for a known location dataset in a supervised manner and
utilized to predict the solar power of an unknown location later. This adaptive
data-driven approach exhibits notable advantages in terms of computing speed,
storage efficiency, and its ability to improve outcomes in scenarios where
state-of-the-art non-adaptive methods fail. Our method has shown an improvement
of $10.47 \%$, $7.44 \%$, $5.11\%$ in solar power prediction accuracy compared
to best performing non-adaptive method for California (CA), Florida (FL) and
New York (NY), respectively.

提出了一个基于领域自适应深度学习的框架来估计太阳能发电量，该框架利用天气特征来解决太阳能发电预测中的挑战。该方法在计算速度、存储效率和改善预测准确性方面在加利福尼亚（CA）、佛罗里达（FL）和纽约（NY）等地的太阳能发电预测中显示出明显优势。

无关位置的无源域自适应学习预测太阳能发电

Location Agnostic Source-Free Domain Adaptive Learning to Predict Solar  Power Generation

Accurately forecasting solar power generation is crucial in the global
progression towards sustainable energy systems. In this study, we conduct a
meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and
classical Long Short-Term Memory (LSTM) models for solar power production
forecasting. Our controlled experiments reveal promising advantages of QLSTMs,
including accelerated training convergence and substantially reduced test loss
within the initial epoch compared to classical LSTMs. These empirical findings
demonstrate QLSTM's potential to swiftly assimilate complex time series
relationships, enabled by quantum phenomena like superposition. However,
realizing QLSTM's full capabilities necessitates further research into model
validation across diverse conditions, systematic hyperparameter optimization,
hardware noise resilience, and applications to correlated renewable forecasting
problems. With continued progress, quantum machine learning can offer a
paradigm shift in renewable energy time series prediction. This pioneering work
provides initial evidence substantiating quantum advantages over classical
LSTM, while acknowledging present limitations. Through rigorous benchmarking
grounded in real-world data, our study elucidates a promising trajectory for
quantum learning in renewable forecasting. Additional research and development
can further actualize this potential to achieve unprecedented accuracy and
reliability in predicting solar power generation worldwide.

准确预测太阳能发电对于可持续能源系统的全球发展至关重要。本研究比较了量子长短期记忆（QLSTM）和经典长短期记忆（LSTM）模型在太阳能发电预测中的表现，结果显示 QLSTM 具有训练收敛加速和测试损失显著降低等优点。然而，要充分发挥 QLSTM 的潜力，需要进一步研究模型验证、系统超参数优化、硬件噪声韧性和相关可再生能源预测应用。通过严格基于真实数据的基准测试，本研究揭示了量子学习在可再生能源预测中的一个有前景的轨迹。进一步的研究和开发可以进一步实现该潜力，实现对全球太阳能发电的前所未有的准确性和可靠性预测。