Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation

Photovoltaic (PV) power generation is highly intermittent in nature and any accurate very short-term prediction can decrease the impact of its uncertainties and operation costs and boost the reliable and efficient integration of PV systems into micro/smart grids. This work develops a new generalized technique for very short-term prediction of PV power generation from the lagged power generation data using fuzzy techniques. A preprocessor extracts relevant statistical features from the PV data which are fed to the fuzzy predictor. A modified version of Wang-Mendel training algorithm is employed to directly extract the fuzzy rules from the training data pairs. This methodology exploits the limited training data more efficiently. In addition, an online additive learning routine is proposed, which enables the predictor to learn from new data while running the predictions. So, the prediction accuracy increases over time and the predictor updates to account for long-term changing conditions of weather and PV system performance and its surroundings. Numerical results of the comparison of the proposed approach with simple fuzzy and traditional artificial neural network methods on a live PV system in the United Kingdom demonstrate its improved prediction accuracy, outperforming the benchmark approaches with a normalized mean absolute error (NMAE) of 3.6%

Technology Agnostic Analysis and Design for Improved Performance, Variability, and Reliability in Thin Film Photovoltaics

Thin film photovoltaics (TFPV) offer low cost alternatives to conventional crystalline Silicon (c-Si) PV, and can enable novel applications of PV technology. Their large scale adoption however, requires significant improvements in process yield, and operational reliability. In order to address these challenges, comprehensive understanding of factors affecting panel yield, and predictive models of performance reliability are needed. This has proved to be especially challenging for TFPV for two reasons in particular. First, TFPV technologies encompass a wide variety of materials, processes, and structures, which fragments the research effort. Moreover, the monolithic manufacturing of TFPV modules differs significantly from that of c-Si technology, and requires new integrated approaches to analysis and design for these technologies. In this thesis, we identify a number of features affecting the variability and reliability of TFPV technologies in general, and propose technology agnostic design solutions for improved performance, yield, and lifetime of TFPV modules. We first discuss the universal features of current conduction in TFPV cells, for both intrinsic dark and light currents, and parasitic (shunt) leakage. We establish the universal physics of space-charge-limited shunt conduction in TFPV technologies, and develop physics based compact model for TFPV cells. We examine the statistics of parasitic shunting, and demonstrate its universal log-normal distribution across different technologies. We also evaluate the degradation behavior of cells under reverse bias stress, and identify different degradation mechanisms for intrinsic and parasitic components. We then embed the physics and statistics of cell operation and degradation, in a circuit simulation framework to analyze module performance and reliability. With this integrated circuit-device simulation, we establish log-normal shunt statistics as a major cause of module efficiency loss in TFPV, and develop a in-line technique for module efficiency and yield enhancement. Finally, we study the features of TFPV module reliability under partial shading using this circuit simulation, and propose a geometrical design solution for shade tolerant TFPV modules. The most important theme of this thesis is to establish that TFPV technologies share many universal performance, variability, and reliability challenges. And, by using a technology agnostic approach for studying these problems, we can achieve fruitful cross coupling of ideas and enable broadly applicable solutions for important technological challenges in TFPV

NASA Capability Roadmaps Executive Summary

This document is the result of eight months of hard work and dedication from NASA, industry, other government agencies, and academic experts from across the nation. It provides a summary of the capabilities necessary to execute the Vision for Space Exploration and the key architecture decisions that drive the direction for those capabilities. This report is being provided to the Exploration Systems Architecture Study (ESAS) team for consideration in development of an architecture approach and investment strategy to support NASA future mission, programs and budget requests. In addition, it will be an excellent reference for NASA's strategic planning. A more detailed set of roadmaps at the technology and sub-capability levels are available on CD. These detailed products include key driving assumptions, capability maturation assessments, and technology and capability development roadmaps

Previsão de geração fotovoltaica através de métodos computacionais parametrizados por planejamento de experimentos

In recent years, renewable and sustainable energy sources have attracted the attention of various investors and stakeholders, such as energy sector players and consumers. Electric power systems have experienced the rapid insertion of distributed renewable generating sources and, as a result, face planning and operational challenges as new connections are made to the grid. It is very difficult to observe and anticipate the required levels of photovoltaic generation, which are tasks considered inherent to a quick insertion into the electrical grid. This distributed/renewable generation must be integrated in a coordinated way, so that there is no negative impact on the electrical performance of the grid, increas ing the complexity of energy management. In this work, a multivariate strategy, based on design of experiments (DOE), is addressed for the prediction of photovoltaic generation using a new approach for parameterization and combination of a set of artificial neural networks (ANN). Two main questions will be explored: how to select the ANNs and how to combine them in the forecast by sets (ensemble). As a complement to this methodology, the reduction of dimensionality of climate data through Principal Component Analysis (PCA) is also presented. The design of experiments (DOE) approach is applied to the PV generation time series factors and to the ANN factors. Then, a cluster analysis is performed to select the networks that obtained the best results. From this point, a mixture analysis (MDE) is used to determine the ideal weights for the formation of the ensemble. The methodology is detailed throughout the work and, based on the combination of fore casts, the photovoltaic generation was estimated for a set of specific panels, located in the south of the State of Minas Gerais. Therefore, a more comprehensive study, which con sidered a dataset of seventeen generation plants, with seasonal characteristics, was also examined. The versatility of the proposed method allowed changing the number of factors to be used in the experimental arrangement, in the forecasting model and in the desired forecasting horizon and, consequently, improving the determination of the forecast for the studied scenarios.Nos últimos anos, as fontes de energia renováveis e sustentáveis atraíram a atenção de vários investidores e partes interessadas, como agentes do setor de energia e consumidores. Os sistemas de energia elétrica têm experimentado a rápida inserção de fontes geradoras renováveis distribuídas e, como resultado, enfrentam desafios de planejamento e operação à medida que novas conexões são feitas à rede. É de grande dificuldade observar e antecipar os níveis exigidos de geração fotovoltaica, que são tarefas consideradas inerentes a uma rápida inserção na rede elétrica. Essa geração distribuída/renovável deve ser integrada de forma coordenada, de modo que não haja impacto negativo no desempenho elétrico da rede, aumentando a complexidade do gerenciamento de energia. Neste trabalho, uma estratégia multivariada, baseada em planejamento de experimentos (DOE), é endereçada para a previsão de geração fotovoltaica usando uma nova abordagem para parametrização e combinação de um conjunto de redes neurais artificiais (RNA). Duas questões principais serão exploradas: como selecionar as RNAs e como combiná-las no ensemble. Como complemento dessa metodologia, também é apresentada a redução de dimensionalidade dos dados climáticos através de Análise de Componentes Principais (PCA). A abordagem de planejamento de experimentos (DOE) é aplicada aos fatores da série temporal de geração fotovoltaica e aos fatores da RNA. Em seguida, é realizada uma análise de cluster para selecionar as redes que obtiveram os melhores resultados. A partir deste ponto, uma análise de mistura (MDE) é empregada para determinar os pesos ideais para a formação da previsão por conjunto ensemble. A metodologia é detalhada ao longo do trabalho e, com base na combinação de previsões, foi estimada a geração fotovoltaica para um conjunto de painéis específicos, localizado no sul do Estado de Minas Gerais. Por conseguinte, um estudo mais abrangente, que considerou um conjunto de dados de dezessete plantas de geração, com características sazonais, também foi examinado. A versatilidade do método proposto permitiu a alteração do número de fatores a serem utilizados no arranjo experimental, no modelo de previsão e no horizonte de previsão desejado e, consequentemente, aprimorou a determinação da previsão para os cenários estudados

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

Case studies for developing globally responsible engineers

Document realitzat amb un ajut financer de la Unió Europea. Per poder accedir al material complementari per a docents dels 28 casos estudi que formen el llibre, activeu el "Document relacionat"Col·lecció de 28 casos estudi per a professors d'enginyeria: 1. Rural development and planning in LDCs: the “Gamba Deve – Licoma axis”,district of Caia, Mozambique 2. Reducing the impact of soil erosion and reservoir siltation on agricultural production and water availability: the case study of the Laaba catchment (Burkina Faso) 3. Trade and Mobility on the Rooftop of the World: Gravity Ropeways in Nepal 4. Sustainable Development of Agriculture and Food systems with regard to Water 5. Conservation agriculture: a complex avenue to conserve and improve soils 6. The national rural water supply and sanitation programme in Tanzania 7. Use of statistical tools in a development context. Analysis of variance (ANOVA) 8. Water supply system in Kojani Island (Zanzibar, Tanzania) 9. Faecal sludge management in Lusaka, Zambia 10. Water balance on the Central Rift Valley 11. Rural electrification in developing countries via autonomous micro-grids 12. Photovoltaics electrification in off-grid areas 13. Development of a MILP model to design wind-photovoltaic stand-alone electrification projects for isolated communities in developing countries 14. Estimation if indoor air pollution and health impacts due to biomass burning in rural Northern Ghana 15.Improved cookstoves assessment 16. Supporting the adoption of clean cookstoves and fuels: why won’t people adopt the perfect stove? 17. Do-it-yourself approach as appropriate technology for solar thermal system: the example of CDF Médina, Dakar (Senegal) 18. Essential oil extraction with concentrating solar thermal energy 19. Survival in the desert sun: cool food storage 20. Energy roadmap in Ghana and Botswana 21. Social & ethical issues in engineering 22. Radio communications systems in rural environments 23. A Diffserv transport network to bring 3G access to villages in the Amazon forest 24. Finding the poynting’s theorem in a health centre in San Pablo (Peru) 25. Tanzania, Water and health 26. Flood assessment and warning system 27. Technical aspects of municipal solid waste collection: case studies from East Africa 28. Plastic recyclingPeer ReviewedPostprint (published version