Forecasting technology costs via the Learning Curve - Myth or Magic?

To further our understanding of the effectiveness of learning or experience curves to forecast technology costs, a statistical analysis using historical data has been carried out. Three hypotheses have been tested using available data sets that together shed light on the ability of experience curves to forecast future technology costs. The results indicate that the Single Factor Learning Curve is a highly effective estimator of future costs with little bias when errors were viewed in their log format. However, it was also found that due to the convexity of the log curve an overestimation of potential cost reductions arises when returned to their monetary units. Furthermore the effectiveness of increasing weights for more recent data was tested using Weighted Least Squares with exponentially increasing weights. This resulted in forecasts that were typically less biased than when using Ordinary Least Square and highlighted the potential benefits of this method

Forecasting technology costs via the Learning Curve – Myth or Magic?

To further our understanding of the effectiveness of learning or experience curves to forecast technology costs, a statistical analysis using historical data has been carried out. Three hypotheses have been tested using available data sets that together shed light on the ability of experience curves to forecast future technology costs. The results indicate that the Single Factor Learning Curve is a highly effective estimator of future costs with little bias when errors were viewed in their log format. However it was also found that due to the convexity of the log curve an overestimation of potential cost reductions arises when returned to their monetary units. Furthermore the effectiveness of increasing weights for more recent data was tested using Weighted Least Squares with exponentially increasing weights. This resulted in forecasts that were typically less biased than when using Ordinary Least Square and highlighted the potential benefits of this method.Forecasting, Learning curves, Renewable energy

Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization

This study delves into the shift from centralized to decentralized approaches in the electricity industry, with a particular focus on how machine learning (ML) advancements play a crucial role in empowering renewable energy sources and improving grid management. ML models have become increasingly important in predicting renewable energy generation and consumption, utilizing various techniques like artificial neural networks, support vector machines, and decision trees. Furthermore, data preprocessing methods, such as data splitting, normalization, decomposition, and discretization, are employed to enhance prediction accuracy. The incorporation of big data and ML into smart grids offers several advantages, including heightened energy efficiency, more effective responses to demand, and better integration of renewable energy sources. Nevertheless, challenges like handling large data volumes, ensuring cybersecurity, and obtaining specialized expertise must be addressed. The research investigates various ML applications within the realms of solar energy, wind energy, and electric distribution and storage, illustrating their potential to optimize energy systems. To sum up, this research demonstrates the evolving landscape of the electricity sector as it shifts from centralized to decentralized solutions through the application of ML innovations and distributed decision-making, ultimately shaping a more efficient and sustainable energy future

Big Data Research in Italy: A Perspective

The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains

Big Data in Power Systems: Leveraging grid optimization and wave energy integration

Power systems have been through different challenges and technological innovations in the last years and are rapidly evolving into digital systems through the deployment of the smart grids concept. Producing large amounts of data, power systems can benefit from the application of big data analytics which can help leveraging the optimization processes going on in power grids nowadays. The whole value of chain of electric power can benefit from the application of big data techniques. This paper presents a short overview of possible applications and challenges that still need to be considered for this synergy to grow. Under the framework of an H2020 funded project named BigDataOcean, a case study will be described, showing how a data-driven approach can foster the development of offshore renewable sources using the example of wave energy

The role of intelligent systems in the development of peer-to-peer systems for energetic distribution management

Intelligent Systems are one of today’s greatest strengths, while environmental sustainability is one of today’s biggest challenges. This study aims to integrate the most recent innovations in intelligent technologies with the development of smart energy grids and Peer-to-Peer (P2P) systems for energetic distribution. Specifically, it investigates the complex relations between these concepts, while analysing how developments in each field both influence and are influenced by each other. To do so, this study answers three research questions. The first one regards the implementation of Intelligent Systems, the second concerns the development of Smart Grids, while the third is concerned with the possibility of building P2P Systems. To provide relevant conclusions, an extensive literature review regarding all subjects was carried, along with a statistical analysis of three online surveys. The obtained results show that there are significant influences and connections between the development of intelligent technologies and the implementation of smart grids and P2P Systems, thus supporting several hypotheses formulated for this study. On this basis, conclusions are drawn concerning the high value of each topic in separate, and the even higher value of the topics when integrated.Sistemas Inteligentes são um dos maiores benefícios dos dias de hoje, enquanto a sustentabilidade ambiental é um dos maiores desafios. Este estudo pretende integrar as mais recentes inovações em tecnologias inteligentes com o desenvolvimento de redes de energia inteligentes (Smart Grids) e sistemas Peer-to-Peer (P2P) para distribuição energética. Especificamente, investiga as relações complexas entre estes conceitos, enquanto analisa como desenvolvimentos em cada área influenciam e são influenciados pelas outras. Para isso, este estudo responde a três questões de pesquisa. A primeira relaciona-se com a implementação de Sistemas Inteligentes, a segunda com o desenvolvimento de Redes Inteligentes, e a terceira está relacionada com a possibilidade de construir Sistemas P2P. Para obter conclusões relevantes, foi feita uma extensa revisão de literatura relativa a todos os temas, assim como uma análise estatística de três questionários online. Os resultados obtidos mostram que existem influências e conexões significativas entre o desenvolvimento de tecnologias inteligentes e a implementação de Smart Grids e Sistemas P2P, suportando assim múltiplas hipóteses formuladas para este estudo. Com esta base, são retiradas conclusões que confirmam o elevado valor de cada tópico em separado, e o ainda maior valor dos tópicos quando integrados

A Supportive Framework for the Development of a Digital Twin for Wind Turbines Using Open-Source Software Tiril Malmedal Mechanics and Process Technology

The world is facing a global climate crisis. Renewable energy is one of the big solutions, nevertheless, there are technological challenges. Wind power is an important part of the renewable energy system. With the digitalization of industry, smart monitoring and operation is an important step towards efficient use of resources. Thus, Digital Twins (DT) should be applied to enhance power output. Digital Twins for energy systems combine many fields of study, such as smart monitoring, big data technology, and advanced physical modeling. Frameworks for the structure of Digital Twins are many, but there are few standardized methods based on the experience of such developed Digital Twins. An integrative review on the topic of Digital Twins with the goal of creating a conceptual development framework for DTs with open-source software is performed. However, the framework is yet to be tested experimentally but is nevertheless an important contribution toward the understanding of DT technology development. The result of the review is a seven-step framework identifying potential components and methods needed to create a fully developed DT for the aerodynamics of a wind turbine. Suggested steps are Assessment, Create, Communicate, Aggregate, Analyze, Insight, and Act. The goal is that the framework can stimulate more research on digital twins for small-scale wind power. Thus, making small-scale wind power more accessible and affordable