Renewable energy sources integration via machine learning modelling: A systematic literature review

The use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms of costs and technology, expecting a massive diffusion in the near future and placing several challenges to the power grid. Since RESs depend on stochastic energy sources —solar radiation, temperature and wind speed, among others— they introduce a high level of uncertainty to the grid, leading to power imbalance and deteriorating the network stability. In this scenario, managing and forecasting RES uncertainty is vital to successfully integrate them into the power grids. Traditionally, physical- and statistical-based models have been used to predict RES power outputs. Nevertheless, the former are computationally expensive since they rely on solving complex mathematical models of the atmospheric dynamics, whereas the latter usually consider linear models, preventing them from addressing challenging forecasting scenarios. In recent years, the advances in machine learning techniques, which can learn from historical data, allowing the analysis of large-scale datasets either under non-uniform characteristics or noisy data, have provided researchers with powerful data-driven tools that can outperform traditional methods. In this paper, a systematic literature review is conducted to identify the most widely used machine learning-based approaches to forecast RES power outputs. The results show that deep artificial neural networks, especially long-short term memory networks, which can accurately model the autoregressive nature of RES power output, and ensemble strategies, which allow successfully handling large amounts of highly fluctuating data, are the best suited ones. In addition, the most promising results of integrating the forecasted output into decision-making problems, such as unit commitment, to address economic, operational and managerial grid challenges are discussed, and solid directions for future research are provided

Forecasting wind energy for a data center

Abstract. Data centers are increasingly using renewables such as wind and solar energy. RISE’s ICE data center has already solar panels and is now studying impact of adding a wind turbine into their microgrid. In this thesis, a machine learning model was developed to forecast wind power production for the data center. Data center in Luleå has several applications to utilize wind power forecasting. Renewable energy sources are intermittent, so accurate forecasting of output power reduces a need for additional balancing of energy and reserve power in an electricity grid. Renewable energy can be reserved from market for next hour or next day to maximize its use. Forecasting from 30 min to 6 hours ahead allows job scheduling to optimize usage of renewables and to reduce power consumption. Data center may target to minimize electricity cost or maximize usage of renewables for lower greenhouse gas emissions. Smart microgrid based on artificial intelligence is the way to implement the applications. Two open data sets from India and Sweden have been used in the research. The data available supports choosing of a statistical model. Random forest regression was the model used in the research. Data from India enabled to develop a model for one wind turbine. Developed model forecasted output power well. Swedish data set is from EEM20 competition, it included total wind power production in Sweden and had to be applied to approximate production of one wind turbine in Luleå. To achieve the goal output power of Luleå price region was averaged, and location for the simulation was chosen to be near Luleå. As expected, the accuracy of forecasting with Swedish data was reasonable, but approximations done reduced it. The developed model was applied to RISE’s ICE data center. Validation has been done, but final testing will take place in RISE’s simulation environment. In general, data from northern Sweden is not openly available for wind power forecasting. In addition, any scientific articles covering the geographical area were not found while working on literature review. The study with Swedish competition data gave understanding, which variables are significant in northern Sweden and about their relative importances. Wind gust is such a variable. Using two data sets from different geographical locations proved that climate has a major impact on performance of the trained model. Thus, it is reasonable to use the trained model in locations with similar weather conditions only.Tuulienergian ennustaminen datakeskusta varten. Tiivistelmä. Datakeskukset käyttävät uusiutuvia energialähteitä yhä enemmän. Tällaisia lähteitä ovat mm. tuuli- ja aurinkoenergia. RISE:n ICE datakeskuksella Luulajassa on jo aurinkopaneelit käytössä, ja nyt tutkitaan tuulimyllyn lisäämisen vaikutusta mikroverkkoon. Tässä työssä kehitettiin koneoppimismalli tuulivoiman tuotannon ennustamiseksi datakeskusta varten. Datakeskuksella on useita sovelluksia tuulienergian ennustamisen hyödyntämiseksi. Uusiutuvat energialähteet ovat luonteeltaan vaihtelevia, joten tuotetun tehon tarkka ennustaminen vähentää ylimääräisen säätämisen ja reservitehon tarvetta sähköverkossa yleensäkin. Datakeskus voi varata uusiutuvaa energiaa markkinoilta seuraavaksi tunniksi tai päiväksi uusiutuvan energian käytön maksimoimiseksi. Ennustaminen 30 minuutista 6 tuntiin etukäteen mahdollistaa työjonon aikatauluttamisen uusiutuvien käytön optimoimiseksi ja vähentää tehonkulutusta. Datakeskus voi pyrkiä minimoimaan sähkön käytön kustannuksia, tai pienentämään kasvihuonekaasujen päästöjä käyttämällä mahdollisimman paljon uusiutuvaa energiaa. Tekoälyyn perustuva älykäs mikroverkko on tapa toteuttaa edellä mainitut sovellukset. Tutkimuksessa on käytetty kahta avointa tietoainestoa Intiasta ja Ruotsista. Saatavilla oleva data tukee tilastollisen ennustemallin valintaa. Tässä työssä käytettiin satunnaismetsämenetelmää. Intian dataa käytettiin mallin kehityksessä yhtä tuulimyllyä varten. Kehitetty malli ennusti tuotetun tehon hyvin. Ruotsalainen data perustuu EEM20-kilpailuun, jossa arvioitiin koko Ruotsin tuulivoiman tuotantoa. Sitä olikin sovellettava Luulajassa olevan yhden tuulimyllyn tuotannon arvioimiseksi. Luulajan hinta-alueen tuottama teho keskiarvoistettiin, ja ennustamista varten valittiin maantieteellinen paikka läheltä Luulajaa. Kuten oli odotettavissa, soveltamisessa tehdyt likiarvoistukset pienensivät ennustamisen tarkkuutta, jota voidaan kuitenkin pitää kohtuullisena. Kehitettyä mallia sovellettiin RISE:n ICE datakeskusta varten. Algoritmin validointi on suoritettu, mutta lopullinen testaus tehdään RISE:n simulointiympäristössä. Yleisesti ennustamiseen soveltuvaa dataa ei ole Pohjois-Ruotsista tarjolla. Tieteellisiä artikkeleita ko. maantieteelliseltä alueelta ei löytynyt kirjallisuustutkimusta tehtäessä. Tutkimus ruotsalaisella datalla toi ymmärrystä siihen, mitkä muuttujat ovat merkittäviä Pohjois-Ruotsin alueella sekä niiden suhteellisesta merkityksestä. Kahden eri maantieteellisen alueen tietoaineiston käyttö osoitti, että ilmastolla on huomattava vaikutus koulutetun mallin suorituskykyyn. Näin onkin mielekästä käyttää koulutettua mallia vain sellaisilla alueilla, joiden sääolosuhteet ovat samankaltaiset

Deep learning architectures applied to wind time series multi-step forecasting

Forecasting is a critical task for the integration of wind-generated energy into electricity grids. Numerical weather models applied to wind prediction, work with grid sizes too large to reproduce all the local features that influence wind, thus making the use of time series with past observations a necessary tool for wind forecasting. This research work is about the application of deep neural networks to multi-step forecasting using multivariate time series as an input, to forecast wind speed at 12 hours ahead. Wind time series are sequences of meteorological observations like wind speed, temperature, pressure, humidity, and direction. Wind series have two statistically relevant properties; non-linearity and non-stationarity, which makes the modelling with traditional statistical tools very inaccurate. In this thesis we design, test and validate novel deep learning models for the wind energy prediction task, applying new deep architectures to the largest open wind data repository available from the National Renewable Laboratory of the US (NREL) with 126,692 wind sites evenly distributed on the US geography. The heterogeneity of the series, obtained from several data origins, allows us to obtain conclusions about the level of fitness of each model to time series that range from highly stationary locations to variable sites from complex areas. We propose Multi-Layer, Convolutional and recurrent Networks as basic building blocks, and then combined into heterogeneous architectures with different variants, trained with optimisation strategies like drop and skip connections, early stopping, adaptive learning rates, filters and kernels of different sizes, between others. The architectures are optimised by the use of structured hyper-parameter setting strategies to obtain the best performing model across the whole dataset. The learning capabilities of the architectures applied to the various sites find relationships between the site characteristics (terrain complexity, wind variability, geographical location) and the model accuracy, establishing novel measures of site predictability relating the fit of the models with indexes from time series spectral or stationary analysis. The designed methods offer new, and superior, alternatives to traditional methods.La predicció de vent és clau per a la integració de l'energia eòlica en els sistemes elèctrics. Els models meteorològics es fan servir per predicció, però tenen unes graelles geogràfiques massa grans per a reproduir totes les característiques locals que influencien la formació de vent, fent necessària la predicció d'acord amb les sèries temporals de mesures passades d'una localització concreta. L'objectiu d'aquest treball d'investigació és l'aplicació de xarxes neuronals profundes a la predicció \textit{multi-step} utilitzant com a entrada series temporals de múltiples variables meteorològiques, per a fer prediccions de vent d'ací a 12 hores. Les sèries temporals de vent són seqüències d'observacions meteorològiques tals com, velocitat del vent, temperatura, humitat, pressió baromètrica o direcció. Les sèries temporals de vent tenen dues propietats estadístiques rellevants, que són la no linearitat i la no estacionalitat, que fan que la modelització amb eines estadístiques sigui poc precisa. En aquesta tesi es validen i proven models de deep learning per la predicció de vent, aquests models d'arquitectures d'autoaprenentatge s'apliquen al conjunt de dades de vent més gran del món, que ha produït el National Renewable Laboratory dels Estats Units (NREL) i que té 126,692 ubicacions físiques de vent distribuïdes per total la geografia de nord Amèrica. L'heterogeneïtat d'aquestes sèries de dades permet establir conclusions fermes en la precisió de cada mètode aplicat a sèries temporals generades en llocs geogràficament molt diversos. Proposem xarxes neuronals profundes de tipus multi-capa, convolucionals i recurrents com a blocs bàsics sobre els quals es fan combinacions en arquitectures heterogènies amb variants, que s'entrenen amb estratègies d'optimització com drops, connexions skip, estratègies de parada, filtres i kernels de diferents mides entre altres. Les arquitectures s'optimitzen amb algorismes de selecció de paràmetres que permeten obtenir el model amb el millor rendiment, en totes les dades. Les capacitats d'aprenentatge de les arquitectures aplicades a ubicacions heterogènies permet establir relacions entre les característiques d'un lloc (complexitat del terreny, variabilitat del vent, ubicació geogràfica) i la precisió dels models, establint mesures de predictibilitat que relacionen la capacitat dels models amb les mesures definides a partir d'anàlisi espectral o d'estacionalitat de les sèries temporals. Els mètodes desenvolupats ofereixen noves i superiors alternatives als algorismes estadístics i mètodes tradicionals.Arquitecturas de aprendizaje profundo aplicadas a la predición en múltiple escalón de series temporales de viento. La predicción de viento es clave para la integración de esta energía eólica en los sistemas eléctricos. Los modelos meteorológicos tienen una resolución geográfica demasiado amplia que no reproduce todas las características locales que influencian en la formación del viento, haciendo necesaria la predicción en base a series temporales de cada ubicación concreta. El objetivo de este trabajo de investigación es la aplicación de redes neuronales profundas a la predicción multi-step usando como entrada series temporales de múltiples variables meteorológicas, para realizar predicciones de viento a 12 horas. Las series temporales de viento son secuencias de observaciones meteorológicas tales como, velocidad de viento, temperatura, humedad, presión barométrica o dirección. Las series temporales de viento tienen dos propiedades estadísticas relevantes, que son la no linealidad y la no estacionalidad, lo que implica que su modelización con herramientas estadísticas sea poco precisa. En esta tesis se validan y verifican modelos de aprendizaje profundo para la predicción de viento, estos modelos de arquitecturas de aprendizaje automático se aplican al conjunto de datos de viento más grande del mundo, que ha sido generado por el National Renewable Laboratory de los Estados Unidos (NREL) y que tiene 126,682 ubicaciones físicas de viento distribuidas por toda la geografía de Estados Unidos. La heterogeneidad de estas series de datos permite establecer conclusiones válidas sobre la validez de cada método al ser aplicado en series temporales generadas en ubicaciones físicas muy diversas. Proponemos redes neuronales profundas de tipo multi capa, convolucionales y recurrentes como tipos básicos, sobre los que se han construido combinaciones en arquitecturas heterogéneas con variantes de entrenamiento como drops, conexiones skip, estrategias de parada, filtros y kernels de distintas medidas, entre otros. Las arquitecturas se optimizan con algoritmos de selección de parámetros que permiten obtener el mejor modelo buscando el mejor rendimiento, incluyendo todos los datos. Las capacidades de aprendizaje de las arquitecturas aplicadas a localizaciones físicas muy variadas permiten establecer relaciones entre las características de una ubicación (complejidad del terreno, variabilidad de viento, ubicación geográfica) y la precisión de los modelos, estableciendo medidas de predictibilidad que relacionan la capacidad de los algoritmos con índices que se definen a partir del análisis espectral o de estacionalidad de las series temporales. Los métodos desarrollados ofrecen nuevas alternativas a los algoritmos estadísticos tradicionales.Postprint (published version