Wind speed forecasting at different time scales: a non parametric approach

The prediction of wind speed is one of the most important aspects when dealing with renewable energy. In this paper we show a new nonparametric model, based on semi-Markov chains, to predict wind speed. Particularly we use an indexed semi-Markov model, that reproduces accurately the statistical behavior of wind speed, to forecast wind speed one step ahead for different time scales and for very long time horizon maintaining the goodness of prediction. In order to check the main features of the model we show, as indicator of goodness, the root mean square error between real data and predicted ones and we compare our forecasting results with those of a persistence model

Нумеричко моделирање нуклеационих особина атмосферског минералног аеросола

Mineral dust particles are one of the most abundant aerosol species in the atmosphere. They are very efficient ice nucleating particles (INPs). A mineralogy-sensitive immersion freezing parameterization in presence of dust particles has been implemented in Dust Regional Atmospheric Model (DREAM). Ice nucleating particle concentration (INPC) was also parameterized using two mineralogy-indifferent immersion freezing, and two deposition nucleation parameterizations. A two-year model dataset of dust vertical profiles in Europe was contributed to a model evaluation study at the European scale. Selected cases in the Mediterranean in April 2016, were analyzed in more detail and compared with the lidar-derived vertical profiles of cloud relevant dust concentrations and INPC and in situ INPC measurements. Predicted INPC values were compared to the ice crystal number concentration (ICNC) vertical profiles product during satellite overpasses over the dust plume. Ground-based cloud radar observations of ice water content (IWC) and satellite observations of ice water path (IWP) were used in a qualitative assessment of INPC and observed cloud correlation. While all three model setups agreed within one order of magnitude, the mineralogy-sensitive setup presented a sharp maximum in INPC at -25°C and the sharpest decrease of INPC at temperatures higher than -20°C, due to sensitivity to feldspar. It showed agreement with the in situ measurements at temperatures lower than -20°. It was also the most successful in predicting the ICNC profile shape and extent in the presented cases. Variations in the feldspar content influence the effectiveness of dust as an INP but this effect is reduced by the sedimentation of feldspar silt particles. The horizontal distribution of INPs was well predicted by all the model setups. The differences due to deposition nucleation parameterizations and feldspar content were more pronounced above sea surfaces, over the Atlantic, the Mediterranean and the Caspian Sea.Честице минералног аеросола су један од најприсутнијих типова аеросола у атмосфери. Оне су веома ефикасна језгра нуклеације (INP). Параметризација имерзионог замрзавања у зависности од минералног састава честица песка је укључена у Dust Regional Atmospheric Model (DREAM). Концентрација језгара нуклеације (INPC) је такође параметризована коришћењем две параметризације имерзионог замрзавања и две параметризације депозиционе нуклеације које су индиферентне на састав песка. Подаци добијени двогодишњим симулацијама моделом су део студије евалуације модела регионалних размера у Европи. Одабрани случајеви у Медитерану током априла 2016. године су детаљније анализирани и поређени са вертикалним профилима концентрација песка релевантним за коришћене параметризације и INPC добијеним мерењима лидаром и in situ мерењима INPC. Прогнозиране вредности INPC су поређене са вертикалним профилима концентрација ледених кристала (ICNC) добијених током сателитских прелета изнад перјанице песка. Извршено је квалитативно поређење INPC са осмотреним облацима на основу мерења садржаја леда (IWP) радаром са земље и сателитским осматрањима садржаја леда у стубу ваздуха (IWC). Сви избори параметризација у моделу се слажу до једног реда величине, међутим, минералошки осетљива параметризација показује оштар максимум у INPC на -25°C и најстрмији пад INPC на температурама изнад -20°C због осетљивости на активност фелдспара. Ова параметризација се слаже са in situ мерењима на температурама нижим од -20°C. Такође је најуспешнија у представљању облика и вертикалне распрострањености ICNC у приказаним случајевима. Промене у садржају фелдспара утичу на ефикасност песка као INP али овај ефекат је смањен седиментацијом фелдспара у честицама прашине. Хоризонтална распрострањеност INP је добро прогнозирана у свим подешавањима модела. Разлике због избора шеме за депозициону нуклеацију и због садржаја фелдспара су више изражене изнад водених површина, изнад Атлантика, Медитерана и Каспијског језера

Artificial intelligence in wind speed forecasting: a review

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values

Integrated PHEV Charging Loads Forecasting Model and Optimization Strategies

In this dissertation, an integrated Plug-in Electric Vehicle (PHEV) charging loads forecasting model is developed for regular distribution level system and microgrid system. For regular distribution system, charging schedule optimization is followed up. The objectives are 1. Better cooperation with renewable energy sources (especially wind). 2. Relieving the pressure of current distribution transformers in condition of high penetration level PHEVs. As for microgrid, renewable energy power plants (wind, solar) plays a more important role than regular system. Due to the fluctuation of solar and wind plants\u27 output, an empirical probabilistic model is developed to predict their hourly output. On the other hand, PHEVs are not only considered at the charging loads, but also the discharging output via Vehicle to Grid (V2G) method which can greatly affect the economic dispatch for all the micro energy sources in microgrid. Optimization is performed for economic dispatch considering conventional, renewable power plants, and PHEVs. The simulation in both cases results reveal that there is a great potential for optimization of PHEVs\u27 charging schedule. Furthermore, PHEVs with V2G capability can be an indispensable supplement in modern microgrid

Exploring Machine Learning Models for Wind Speed Prediction

The aim of this work present a comprehensive exploration of machine learning models and compare their performance for wind speed prediction. The prediction is based on variables from atmospheric reanalysis data from a specific wind farm located in Spain as predictive inputs for the system

Ein Beitrag zur systemtechnischen Betrachtung der Windleistungsprognose

Im Rahmen dieser Arbeit wurde der Stand der Wissenschaft aufgearbeitet. Anhand der Analyse konnte festgestellt werden, dass Windleistungsprognosen auf Basis unterschiedlicher Modellkategorien basieren. Ein in über zehn Jahren wenig bis gar nicht veröffentlichtes Thema ist der Einfluss der Datenanalyse insbesondere des Data Minings auf die Prognose. Im Laufe der Bearbeitung des Themas und durch die Erkenntnisse der Literaturrecherche wurde der Fokus auf die Datenanalyse gerichtet. Als Innovation wurden die Daten auf ihre unterschiedliche statistischen Eigenschaften untersucht und diese miteinander kombiniert. Als geeignete Methoden haben sich die multiple Korrelationskoeffizienten (MKK), die bedingte Entropie-Analyse und die Verwendung des linearen, nichtlinearen Informationsmaßes herausgestellt. Nicht geeignet sind die Methoden Hauptkomponentenanalyse (HKA) und Faktorenanalyse. Die Verwendung des MKK sowie des linearen, nichtlinearen Informationsmaßes wurde in keiner recherchierten Quelle verwendet. Mit Hilfe des Box-Jenkins-Verfahrens wurde das ARX Modell (autoregressives Modell mit exogenen Größen) als geeignetes Prognosemodell identifiziert und getestet. Nur durch die Verwendung eines RN (rückgekoppelte neuronale Netze) konnte deren Güte verbessert werden. Für die eingesetzten neuronale Netze wurden Experimente durchgeführt, wie Verfahrensweisen zur weiteren Verbesserung führen können. Abschließend wurde die Metaprognose in innovativer Form eingesetzt und konnte zusätzliche Gütesteigerungen erzielen. Weiterhin wurde eine Kennzahl eingeführt, um den Güteanteil der Datenanalyse und des Prognosemodells in der Prognose zu messen. Dieser zeigt, dass die Datenanalyse ca. 80 % Güteanteil an der Prognose besitzt. Die Prognosemethodik wurde erfolgreich an zwei Photovoltaik-Anlagen getestet. Neben der Datenanalyse wurde der Einfluss der Datenstruktur auf die Prognosequalität bewertet. Ein verlustfreies Speicherformat für Integration historischer Klimaprognosen ist unabdingbar, damit qualitativ performante Prognosemodelle trainiert werden können. Schließlich wurden die Erkenntnisse dieser Arbeit in einen Prototypen MaProSy integriert, mit Hilfe dessen produktive Prognosen umgesetzt werden können.This work processes the latest state of the science. Based on the analysis, it was found that wind power forecasts are based on a wide range of model categories. A topic, which has been heavily neglected over the past ten years, is the impact of data analysis, in particular of data mining, on the prognosis. In the course of working on the topic and the findings of the literature research, the focus was set on data analysis. As an innovation, the data were examined concerning their different statistical characteristics and were combined with each other. Suitable methods have been found to be the multiple correlation coefficient (MKK), the conditional entropy analysis, and the use of the linear, nonlinear information measure. Not suitable are the methods Principal Component Analysis and Factor Analysis. The use of the MKK as well as the linear, nonlinear information measure was not used in any researched source. The Box-Jenkins method was used to identify and test the ARX model (autoregressive model with exogenous parameters) as a suitable predictive model. Only by using an RN (feedback neural networks) their quality could be improved. For the neural networks used, experiments were carried out on how procedures can lead to further improvement. Finally, the metaprognosis was used in an innovative way which resulted in an additional increases in quality. Furthermore, an indicator was introduced to measure the ratio of quality of the data analysis and the forecasting model on the forecast. This shows that the data analysis has a share of about 80 % in the forecast. The forecasting methodology was successfully tested on two photovoltaic systems. In addition to the data analysis, this work evaluates the influence of the data structure on the forecast quality. An indispensable requirement to implement high-performance forecasting models is the lossless storage format for the integration of historical climate forecasts. Finally, the findings of this work were integrated into a prototype MaProSy which performs productive forecasts