A comparison between reconstruction methods for generation of synthetic time series applied to wind speed simulation

Wind energy is an attractive renewable sources and its prediction is highly essential for multiple applications. Over the literature, there are several studies have been focused on the related researches of synthetic wind speed data generation. In this research, two reconstruction methods are developed for synthetic wind speed time series generation. The modeling is constructed based on different processes including independent values generation from the known probability distribution function, rearrangement of random values and segmentation. They have been named as Rank-wise and Step-wise reconstruction methods. The proposed methods are explained with the help of a standard time series and the examination on wind speed time series collected from Galicia, the autonomous region in the northwest of Spain. Results evidenced the potential of the developed models over the state-of-the-art synthetic time series generation methods and demonstrated a successful validation using the means of mean and median wind speed values, autocorrelations, probability distribution parameters with their corresponding histograms and confusion matrix. Pros and cons of both methods are discussed comprehensively

The state-of-the-art progress in cloud detection, identification, and tracking approaches: a systematic review

A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too

Forecasting different dimensions of liquidity in the intraday electricity markets: A review

Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models

The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix

Performance of Parabolic Trough Collector with Different Heat Transfer Fluids and Control Operation

Electricity generation from solar energy has become very desirable because it is abundantly available and eco-friendly. Mathematical modeling of various components of a Solar Thermal Power plant (STP) is warranted to predict the optimal and efficient operation of the plant. The efficiency and reliability of STPs are maximized based on different operating strategies. Opting for proper Heat Transfer Fluid (HTF), which is proposed in this paper, helps in reducing operating complexity and lowering procurement cost. The Parabolic Trough Collector (PTC) is the heart of STP, where proper focusing of PTC towards solar radiation is the primary task to maximize the outlet temperature of HTF. This maximum temperature plays a major factor due to diurnal solar radiation variation, and its disturbance nature, with the frequent startup and shutdown of STP, is avoided. In this paper, the PTC component is modeled from the first principle, and, with different HTF, the performance of PTC with constant and quadratic solar disturbances is analyzed along with classical control system designs. Through this, the operator will be able to choose proper HTF and resize the plant components depending on plant location and weather conditions. Furthermore, the thermal energy is collected for therminol oil, molten salt, and water; and its performance with different inputs of solar radiation is analyzed along with closed-loop controllers. Thermal energy extracted by therminol oil, molten salt, and water with constant solar radiation results in 81.7%,73.7% and 18.7%, respectively