When planning a solar energy conversion system, having sufficiently reliable values of the monthly average daily solar radiation (MADSR) on a horizontal surface is essential. Traditionally, estimates based on other climatological variables for which more information is available have been relied upon to compensate for the lack of direct solar radiation measurements. Solar radiation varies widely, which requires the creation of site-specific forecast models. By using artificial neural network (ANN) models or similar methods using historical datasets, the monthly average daily solar radiation can be easily assessed. To verify the validity of the established ANN model, a series of analyses was performed using the mean squared error, the coefficient of determination (R2), and the mean absolute error. The study used a dataset collected from nine weather stations in Saudi Arabia from 1985 to 2000. The input parameters for the ANN model were the maximum air relative humidity, latitude, the maximum ambient air temperature, longitude, the minimum ambient air temperature, the minimum air relative humidity, sunshine duration, location altitude, and the corresponding month. The R2 for the whole test dataset was 0.8449. Furthermore, a sensitivity analysis using the established ANN model showed that site elevation (location altitude) had the most significant effect on MADSR on a horizontal surface, with a contribution value of 14.66%. The analysis results show that the ANN model accurately estimates MADSR on horizontal surfaces regardless of seasonal variations in weather conditions. Furthermore, this work is important not only for its contribution to the shape of information in solar radiation forecasting but also for establishing the practical application of ANNs in renewable energy management. The results of this work will help improve the utilization of solar energy and support sustainable energy efforts. Furthermore, the proposed ANN model is believed to be useful for predicting MADSR on horizontal surfaces in other locations in Saudi Arabia with similar climatic conditions to the study sites. Furthermore, the ANN approach may be functional to the basic strategy of a solar arrangement and is suitable for forecasting other meteorological data.

The spatial and temporal characteristics of wind and solar energy droughts across the contiguous U.S. and most of Canada for the period 1959–2022 are investigated using bias-corrected values of daily wind and solar power generation derived from the ERA5 meteorological reanalysis. The analysis domain has been divided into regions that correspond to four major interconnects and nine sub-regions. Droughts are examined for wind alone, solar alone, or a mix of wind and solar in which each provides 50% of the long-term mean energy produced, for durations of 1–90 days. Wind and solar energy droughts and floods are characterized on a regional basis through intensity–duration–frequency curves. Wind and solar generation are shown to be weakly anti-correlated over most of the analysis domain, with the exception of the southwest U.S. The intensities of wind and solar droughts are found to be strongly dependent on region. In addition, the wind resource in the central U.S. and the solar resource in the southwestern U.S. are sufficiently good that over-weighting capacity in those areas would help mitigate droughts that span the contiguous United States for most duration lengths. The correlation of droughts for the 50%–50% mix of wind and solar generation with temperature shows that the most intense droughts occur when temperatures exhibit relatively moderate values, not when energy demand will be largest. Finally, for all regions except the southeast U.S., winter droughts will have a larger impact on balancing the electric grid than summer droughts.

Variable renewable energy (VRE) droughts are periods of low renewable electricity production due to natural variability in the weather and climate. These compound renewable energy droughts occur when two or more (typically wind and solar) generation sources are in low availability conditions at the same time. Compound wind and solar droughts are most commonly studied at the hourly and daily timescale due to the short-term nature of energy markets and battery storage capacity. However the seasonal time scale allows for the examination of broader climate and hydrologic patterns that influence a broader renewable energy portfolio and inform the needs for long-duration energy storage. In this study, we use a newly developed dataset of coincident renewable generation to characterize seasonal compound VRE droughts which include wind, solar and hydropower at grid-relevant spatial scales across the contiguous United States (US). Along with the frequency, duration, magnitude, and spatial scale, we specifically examine these climate patterns with a composite climate analysis. Results for the historical period (1982–2019) indicate that seasonal compound VRE droughts can last up to 5 months and occur most frequently in the Fall. While not an established ‘climate stress’ to consider in reliability studies yet, we demonstrate the impact of seasonal energy droughts on a resource adequacy study over the Western US interconnection using a nodal bulk power grid model. We further discuss how seasonal compound VREs can inform the sizing of long-duration energy storage and market incentives to manage short-term extreme events like heat waves and cold snaps while considering seasonal conditions.

Economic productivity depends on reliable access to electricity, but the extreme shortage events of variable wind-solar systems may be strongly affected by climate change. Here, hourly reanalysis climatological data are leveraged to examine historical trends in defined extreme shortage events worldwide. We find uptrends in extreme shortage events regardless of their frequency, duration, and intensity since 1980. For instance, duration of extreme low-reliability events worldwide has increased by 4.1 hours (0.392 hours per year on average) between 1980-2000 and 2001-2022. However, such ascending trends are unevenly distributed worldwide, with a greater variability in low- and middle-latitude developing countries. This uptrend in extreme shortage events is driven by extremely low wind speed and solar radiation, particularly compound wind and solar drought, which however are strongly disproportionated. Only average 12.5% change in compound extremely low wind speed and solar radiation events may give rise to over 30% variability in extreme shortage events, despite a mere average 1.0% change in average wind speed and solar radiation. Our findings underline that wind-solar systems will probably suffer from weakened power security if such uptrends persist in a warmer future.© 2024. The Author(s).

为了强化对复合干旱热浪事件（CDHEs）的监测，选择内蒙古作为研究区，基于115个站点数据，运用标准化降水蒸散指数和高温相对阈值法，对该区干旱和热浪事件进行定义与识别。并在此基础上，构建复合干旱热浪强度指数（CDHMI）进行复合干旱热浪频率（CDHF）统计，继而开展干旱、热浪和CDHEs时空变化特征研究。结果显示：暖季（5—10月）不同时间尺度（1个月和6个月尺度）干旱强度皆呈增加趋势。内蒙古西部和通辽市中部干旱频发，中东部地区干旱发生频率显著增加。热浪频率显著增加、热浪期显著延长，热浪持续时间和强度呈非显著增加趋势。热浪频率、持续时间和强度总体呈现西高东低的空间分布格局。CDHEs发生的频率和强度呈现显著增加趋势，特别是1998年之后。空间分布上，CDHF高值区集中在锡林郭勒盟和呼伦贝尔市，内蒙古北部地区CDHF明显增多，而高强度CDHMI分布在内蒙古东北和西南地区，增长趋势与CDHF相似。

. Many scientists have made use of the wavelet method in analyzing time series, often using popular free software. However, at present there are no similar easy to use wavelet packages for analyzing two time series together. We discuss the cross wavelet transform and wavelet coherence for examining relationships in time frequency space between two time series. We demonstrate how phase angle statistics can be used to gain confidence in causal relationships and test mechanistic models of physical relationships between the time series. As an example of typical data where such analyses have proven useful, we apply the methods to the Arctic Oscillation index and the Baltic maximum sea ice extent record. Monte Carlo methods are used to assess the statistical significance against red noise backgrounds. A software package has been developed that allows users to perform the cross wavelet transform and wavelet coherence (www.pol.ac.uk/home/research/waveletcoherence/).

利用安徽省80站1955～2005年逐日日照时数、云量、水汽压、降水量及合肥市能见度资料，采用t检验、线性趋势分析等统计方法，分析了安徽省日照时数年、季节、月的变化特征以及空间分布特征；同时分析了可能影响日照时数的气象因子的变化特征。结果表明：安徽省年日照时数呈显著减少的趋势，平均美10年减少88.3h；除春季日照时数变化不明显外，其他季节日照时数显著减少。在20世纪60年代末至70年代初和70年代末都存在明显的突变，且都变少。安徽省日照时数大致呈纬向分布，分布形式为北高南低；除淮北市外，其它各站的日照时数均呈下降的趋势，其中下降趋势最大的是淮北的西部和江淮之间中部地区。能见度、大气水汽压、降水量、云量是影响日照时数的因素；日照时数的显著下降与能见度的下降、大气水汽压的增加、年降水量和年雨日数的增加关系密切，而与总云量和低云量的减少无关。