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The deployment of photovoltaic (PV) systems has increased globally to meet renewable energy targets. Intermittent PV power generated due to cloud-induced variability introduces reliability and grid stability issues at higher penetration levels. Variability in power generation can induce voltage fluctuations within the distribution system and cause adverse effects on power quality. Therefore, it is essential to quantify resource variability to mitigate an intermittent power supply. In this study, we propose a new scheme to classify the sky conditions that are based on two common variability metrices: daily clear-sky index and normalized aggregate ramp rates. The daily clear-sky index estimates the cloudiness in the sky, and ramp rates account for the variability introduced in the system generation due to sudden cloud movements. This classification scheme can identify clear-sky, highly variable, low intermittent, high intermittent and overcast days. By performing a Chi-square test on the training and test sets, we obtain Chi-square statistic values greater than 3 with p-value > 0.05. This indicates that the distribution of the training and test clusters are similar, indicating the robustness of the proposed sky classification scheme. We have demonstrated the applicability of the scheme with diverse datasets to show that the proposed classification scheme can be homogenously applied to any dataset globally despite their temporal resolution. Using various case studies, we demonstrate the potential applications of the scheme for understanding resource allocation, site selection, estimating future intermittency due to climate change, and cloud enhancement effects. The proposed sky classification scheme enhances the precision and reliability of solar energy forecasts, optimizing system performance and maximizing energy production efficiency. This improved accuracy is crucial for variability control and planning, ensuring optimal output from PV plants. [ABSTRACT FROM AUTHOR]

The research reported in this article focuses on a comparison of two different approaches to setting error bounds, or prediction intervals, on short-term forecasting of solar irradiation as well as solar and wind farm output. Short term in this instance relates to the time scales applicable in the Australian National Electricity Market (NEM), which operates on a five-minute basis throughout the year. The Australian Energy Market Operator (AEMO) has decided in recent years that, as well as point forecasts of energy, it is advantageous for planning purposes to have error bounds on those forecasts. We use quantile regression as one of the techniques to construct the bounds. This procedure is compared to a method of forecasting the conditional variance by use of either ARCH/GARCH or exponential smoothing, whichever is more appropriate for the specific application. The noise terms for these techniques must undergo a normalising transformation before their application. It seems that, for certain applications, quantile regression performs better, and the other technique for some other applications. [ABSTRACT FROM AUTHOR]

The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV) panels based on a self-organizing feature map (SOFM) model. The proposed model is compared with other models, including the multi-layer perceptron (MLP) and support vector machine (SVM) models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE), mean absolute error (MAE), correlation (R), and coefficient of determination (R²). The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R² value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output. [ABSTRACT FROM AUTHOR]

Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This is especially the case in Australia, where there has been a massive increase in solar and wind farms in the last 15 years, as well as in roof top solar, both domestic and commercial. For example, in 2020, 27% of the electricity in Australia was from renewable sources, and in South Australia almost 60% was from wind and solar. In the literature, there has been extensive research reported on solar and wind resource, entailing both point and interval forecasts, but there has been much less focus on the forecasting of output from wind and solar systems. In this review, we canvass both what has been reported and also what gaps remain. In the case of the latter topic, there are numerous aspects that are not well dealt with in the literature. We have added discussion on the value of forecasts, rather than just focusing on forecast skill. Further, we present a section on how to deal with conditionally changing variance, a topic that has little focus in the literature. One other topic may be particularly important in Australia at the moment, but may become more widespread. This is how to deal with the concept of a clear sky output from a solar farm when the field is oversized compared to the inverter capacity, resulting in a plateau for the output. [ABSTRACT FROM AUTHOR]

Solar energy is an economic and clean power source subject to natural variability, while energy storage might attenuate it, ultimately, effective and operationally feasible forecasting techniques for energy management are needed for better grid integration. This work presents a novel deterministic forecast method considering: irradiance pattern classification, Markov chains, fuzzy logic and an operational approach. The method developed was applied in a rolling manner for six years to a target location with no prior data to assess performance and its changes as new local data becomes available. Clearness index, diffuse fraction and irradiance hourly forecasts are analyzed on a yearly basis but also for 20 day types, and compared against smart persistence. Results show the proposed method outperforms smart persistence by ~10% for clearness index and diffuse fraction on the base case, but there are significant differences across the 20 day types analyzed, reaching up to +60% for clear days. Forecast lead time has the greatest impact in forecasting performance, which is important for any practical implementation. Seasonality in data gaps or rejected data can have a definite effect in performance assessment. A novel, comprehensive and detailed analysis framework was shown to present a better assessment of forecasters' performance. [ABSTRACT FROM AUTHOR]

Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This includes not only forecasting the expected level, but also putting error bounds on the forecast. The National Electricity Market (NEM) in Australia operates on a five minute basis. We used statistical forecasting tools to generate forecasts with prediction intervals, trialing them on one wind and one solar farm. In classical time series forecasting, construction of prediction intervals is rudimentary if the error variance is constant—Termed homoscedastic. However, if the variance changes—Either conditionally as with wind farms, or systematically because of diurnal effects as with solar farms—The task is much more complicated. The tools were trained on segments of historical data and then tested on data not used in the training. Results from the testing set showed good performance using metrics, including Coverage and Interval Score. The methods used can be adapted to various time scales for short term forecasting. [ABSTRACT FROM AUTHOR]

This study proposes the time-varying nonlinear panel unit root test to investigate the convergence of ecological foot prints between the EU and candidate countries. Sixteen European countries (such as Albania, Austria, Belgium, Denmark, France, Germany, Greece, Italy, Luxembourg, Netherlands, Poland, Portugal, Romania, Spain, Sweden and Turkey) and analysis periods are selected according to data availability. This study proposes a cross-sectional Panel KSS with Fourier to test the convergence of the ecological footprints. Then, we combine this methodology with the rolling window method to take into account the time-varying stationarity of series. This study evaluated sub-components of ecological footprints separately and provided more comprehensive findings for the ecological footprint. According to empirical findings, this study proves that convergence or divergence does not show continuity over time. On the other side, this study points out the presence of divergence draws attention when considering the properties of the sub-components in general. As a result, this study shows that international policies by EU countries are generally accepted as successful to reduce ecological footprint, but these are not sufficient as expected. In this point, it is suggested to keep national policies to support international policies in the long term. [ABSTRACT FROM AUTHOR]

With the recent rapid increase in the use of roof top photovoltaic solar systems worldwide, and also, more recently, the dramatic escalation in building grid connected solar farms, especially in Australia, the need for more accurate methods of very short-term forecasting has become a focus of research. The International Energy Agency Tasks 46 and 16 have brought together groups of experts to further this research. In Australia, the Australian Renewable Energy Agency is funding consortia to improve the five minute forecasting of solar farm output, as this is the time scale of the electricity market. The first step in forecasting of either solar radiation or output from solar farms requires the representation of the inherent seasonality. One can characterise the seasonality in climate variables by using either a multiplicative or additive modelling approach. The multiplicative approach with respect to solar radiation can be done by calculating the clearness index, or alternatively estimating the clear sky index. The clearness index is defined as the division of the global solar radiation by the extraterrestrial radiation, a quantity determined only via astronomical formulae. To form the clear sky index one divides the global radiation by a clear sky model. For additive de-seasoning, one subtracts some form of a mean function from the solar radiation. That function could be simply the long term average at the time steps involved, or more formally the addition of terms involving a basis of the function space. An appropriate way to perform this operation is by using a Fourier series set of basis functions. This article will show that for various reasons the additive approach is superior. Also, the differences between the representation for solar energy versus solar farm output will be demonstrated. Finally, there is a short description of the subsequent steps in short-term forecasting. [ABSTRACT FROM AUTHOR]

With ever increasing demand for electricity and the huge potential of renewable energy, an increasing number of renewable-energy sources are being used to generate electricity. However, due to the intermittency of renewable-energy generation, many researchers try to overcome the variable nature of renewable energy. A hybrid renewable-energy system is one possible way to introduce smoothing of the supply. Many hybrid renewable-energy studies focus on system optimization and management. This paper mainly researches the performance prediction accuracy of a hybrid solar and wind system. Through a mixed autoregressive and dynamical system model, we test the predictability of the hybrid system and compare it with individual solar and wind series forecasting. After error analysis, the predictability of the hybrid system shows a better performance than solar or wind for Adelaide global solar radiation and Starfish Hill wind farm data. The prediction errors were reduced by 13% to more than 30% according to various error analyses. This result indicates an advantage of the hybrid solar and wind system compared to solar and wind systems taken individually. [ABSTRACT FROM AUTHOR]