Computational Intelligence for classification and forecasting of solar photovoltaic energy production and energy consumption in buildings

This thesis presents a few novel applications of Computational Intelligence techniques in the field of energy-related problems. More in detail, we refer to the assessment of the energy produced by a solar photovoltaic installation and to the evaluation of building’s energy consumptions. In fact, recently, thanks also to the growing evolution of technologies, the energy sector has drawn the attention of the research community in proposing useful tools to deal with issues of energy efficiency in buildings and with solar energy production management. Thus, we will address two kinds of problem. The first problem is related to the efficient management of solar photovoltaic energy installations, e.g., for efficiently monitoring the performance as well as for finding faults, or for planning the energy distribution in the electrical grid. This problem was faced with two different approaches: a forecasting approach and a fuzzy classification approach for energy production estimation, starting from some knowledge about environmental variables. The forecasting system developed is able to reproduce the instantaneous curve of daily energy produced by the solar panels of the installation, with a forecasting horizon of one day. It combines neural networks and time series analysis models. The fuzzy classification system, rather, extracts some linguistic knowledge about the amount of energy produced by the installation, exploiting an optimal fuzzy rule base and genetic algorithms. The developed model is the result of a novel hierarchical methodology for building fuzzy systems, which may be applied in several areas. The second problem is related to energy efficiency in buildings, for cost reduction and load scheduling purposes, and was tackled by proposing a forecasting system of energy consumption in office buildings. The proposed system exploits a neural network to estimate the energy consumption due to lighting on a time interval of a few hours, starting from considerations on available natural daylight

Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data

With climate change driving an increasingly stronger influence over governments and municipalities, sustainable development, and renewable energy are gaining traction across the globe. This is reflected within the EU 2030 agenda, that envisions a future where there is universal access to affordable, reliable and sustainable energy. One of the challenges to achieve this vision lies on the low reliability of certain renewable sources. While both particulars and public entities try to reach self-sufficiency through sustainable energy generation, it is unclear how much investment is needed to mitigate the unreliability introduced by natural factors such as varying wind speed and daylight across the year. In this sense, a tool that aids predicting the energy output of sustainable sources across the year for a particular location can aid greatly in making sustainable energy investments more efficient. In this paper, we make use of Open Data sources, Internet of Things (IoT) sensors and installations distributed across Europe to create such tool through the application of Artificial Neural Networks. We analyze how the different factors affect the prediction of energy production and how Open Data can be used to predict the expected output of sustainable sources. As a result, we facilitate users the necessary information to decide how much they wish to invest according to the desired energy output for their particular location. Compared to state-of-the-art proposals, our solution provides an abstraction layer focused on energy production, rather that radiation data, and can be trained and tailored for different locations using Open Data. Finally, our tests show that our proposal improves the accuracy of the forecasting, obtaining a lower mean squared error (MSE) of 0.040 compared to an MSE 0.055 from other proposals in the literature.This paper has been co-funded by the ECLIPSE-UA (RTI2018-094283-B-C32) project from the Spanish Ministry of Science, Innovation, and Universities; both Jose M. Barrera (I-PI 98/18) and Alejandro Reina (I-PI 13/20) hold an Industrial PhD Grants co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

Distributed energy resources and the application of AI, IoT, and blockchain in smart grids

Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability

Industry 4.0 enabling sustainable supply chain development in the renewable energy sector:A multi-criteria intelligent approach

The aim of this paper is to provide a multi-criteria decision-making intelligent approach based on Industry 4.0 and Triple Bottom Line principles for sustainable supply chain development in the renewable energy sector. In particular, the solar photovoltaic energy supply chain is used as a case study, encompassing the entire energy production process, from supply to disposal. An exhaustive literature review is conducted to identify the main criteria affecting social, economic and environmental sustainability in the photovoltaic energy supply chain, and to explore the potential impact of Industry 4.0 on sustainability. Subsequently, three Fuzzy Inference Systems combining quantitative and qualitative data are built to calculate the supply chain's social, economic and environmental sustainability. Experts' opinions are used to identify the impact of Industry 4.0 technologies on the three pillars of sustainability for each supply chain stage. Finally, a novel sustainability index, Sustainability Index 4.0, is formulated to compute the overall sustainability of the photovoltaic energy supply chain in seven countries. The results show the applicability and usefulness of the proposed holistic model in helping policy makers, stakeholders and users to make informed decisions for the development of sustainable renewable energy supply chains, taking into account the impact of Industry 4.0 and digital technologies

PV System Design and Performance

Photovoltaic solar energy technology (PV) has been developing rapidly in the past decades, leading to a multi-billion-dollar global market. It is of paramount importance that PV systems function properly, which requires the generation of expected energy both for small-scale systems that consist of a few solar modules and for very large-scale systems containing millions of modules. This book increases the understanding of the issues relevant to PV system design and correlated performance; moreover, it contains research from scholars across the globe in the fields of data analysis and data mapping for the optimal performance of PV systems, faults analysis, various causes for energy loss, and design and integration issues. The chapters in this book demonstrate the importance of designing and properly monitoring photovoltaic systems in the field in order to ensure continued good performance

Development of AI-Based Tools for Power Generation Prediction

This study presents a model for predicting photovoltaic power generation based on meteorological, temporal and geographical variables, without using irradiance values, which have traditionally posed challenges and difficulties for accurate predictions. Validation methods and evaluation metrics are used to analyse four different approaches that vary in the distribution of the training and test database, and whether or not location-independent modelling is performed. The coefficient of determination,R2, is used to measure the proportion of variation in photovoltaic power generation that can be explained by the model’s variables, while gCO2eq represents the amount of CO2 emissions equivalent to each unit of power generation. Both are used to compare model performance and environmental impact. The results show significant differences between the locations, with substantial improvements in some cases, while in others improvements are limited. The importance of customising the predictive model for each specific location is emphasised. Furthermore, it is concluded that environmental impact studies in model production are an additional step towards the creation of more sustainable and efficient models. Likewise, this research considers both the accuracy of solar energy predictions and the environmental impact of the computational resources used in the process, thereby promoting the responsible and sustainable progress of data science.This research is supported by the Bulgarian National Science Fund in the scope of the project ”Exploration the application of statistics and machine learning in electronics” under contract number κπ-06-H42/1

Economy of grid-connected photovoltaic systems and comparison of irradiance/electric power predictions vs. experimental results

This thesis is focused on various aspects concerning the Distributed Generation (DG) from Renewable Energy Sources (RES) and in particular from PhotoVoltaics (PV). The PV generation strongly depends on weather conditions (irradiance and temperature), therefore the solar irradiance forecast is very important for grid-connected PV systems. The PV power injected into the grid is concentrated during sunlight hours, in which the maximum peak load demand occurs and, as a consequence, an impact on the electrical system occurs. The task of the Transmission System Operator (TSO) is to ensure a constant balance between supply and consumption within the grid. Therefore, the presence of strong fluctuations of the solar radiation requires additional regulatory actions and compensation, through the use of short-term power backup, causing an increase in network costs. Thus, the solar irradiance forecast is necessary for an accurate evaluation of the PV power from PV systems, for the management of electrical grids in order to minimize the costs of energy imbalance and for the decisions concerning the energy market. This thesis essentially consists of two parts. In the first part, the profitability of investments in the rooftop grid-connected PV systems subjected to incentive and the grid-parity analysis in the two main European PV markets (Italy and Germany) are presented. In the second part, in order to minimize the costs of energy imbalance in the Italian electricity market, the comparison of irradiance and electric power predictions with respect to the experimental results of grid-connected PV systems is presented

Impactos del Cambio Climático en la Generación de Energía Renovable y Evaluación de Escenarios de Generación Energética

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, leída el 26-04-2022This Thesis was titled Climate Change Impacts on Renewable Energy Generation and Energy Generation Scenarios.Climate change is attributed, among other factors, to greenhouse gas emissions produced by the energy sector (including the transport). At the same time, climate change is expected to affect this sector by changing the availability of resources, altering its enabling conditions and transforming demand patterns. This thesis addresses climate change impacts on renewable generation and electricity demand by providing an overview of the most relevant transformations projected in literature and by developing methodologies and quantitative analysis to ascertain the specific infuence in three case studies.The first and second chapters are focus on estimating climate change impacts in wind and photovoltaic generation in specific plants. Both provide physical and economic projections of expected changes, along with conclusions for the development of energy policies. The last chapter delves into how climate change and the scenarios proposed to curb it, can affect the demand for electricity in a region, due to the expected changes in the generation infrastructure and changes on the demand side such as a high penetration of electric vehicles...Esta Tesis se tituló Impactos del Cambio Climático en la Generación de Energía Renovable y Escenarios de Generación de Energía. El cambio climático se atribuye, entre otras variables, a las emisiones de gases de efecto invernadero producidas por el sector energético (incluyendo el transporte). Al mismo tiempo, el cambio climático se espera que pueda afectar a este sector cambiando la disponibilidad de sus recursos, alterando sus condiciones habilitantes y transformando los patrones de la demanda. Esta Tesis aborda los impactos del cambio climático en la generación renovable y cambios en el comportamiento de la demanda de electricidad, proporcionando una introducción a las transformaciones más relevantes proyectadas por la literatura y desarrollando metodologías y análisis cuantitativos que determinan el impacto específico en tres casos de estudio. El primer y el segundo capítulo se centran en determinar los cambios esperados en la generación eólica y fotovoltaica en plantas específicas, con especial atención en el calentamiento global. Ambos proporcionan proyecciones físicas y económicas de los cambios esperados, junto con conclusiones para el desarrollo de políticas energéticas. El último capítulo profundiza en cómo el cambio climático y los escenarios propuestos para frenarlo, pueden afectar a la demanda de electricidad de una región, debido a los cambios esperados en las infraestructuras de generación y en cambios por el lado de la demanda como sería una elevada penetración de los vehículos eléctricos...Fac. de Ciencias Económicas y EmpresarialesTRUEunpu

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies