Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques

The growing demand for renewable energy sources, especially wind and solar power, has increased the requirement for precise forecasts in the energy production process. Using machine learning (ML)techniques offers a revolutionary way to deal with this problem, and this thesis uses machinelearning (ML) to estimate solar energy production with the goal of revolutionizing decision-making processes through the analysis of large datasets and the generation of accurate forecasts.Solar meteorological data is analyzed methodologically using regression, time series analysis, and deep learning algorithms. The study demonstrates how well machine learning-based forecasting works to anticipate future solar energy output. Quantitative evaluations show excellent prediction accuracy and verify the techniques used. For example, the key observations made were that the Multiple Linear Regression methods demonstrates reasonable predictive ability with moderate Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values yet slightly lower R-squared values compared to other methods.The study also provides a reflective analysis of result significance, methodology dependability, and result generalizability, as well as a summary of its limits and recommendations for further study. The conclusion provides implications for broader applications across energy sectors and emphasizes the critical role that ML-based forecasting plays in predicting solar energy generation. By utilizing renewable energy sources like solar power, this approach aims to lessen dependency on non-renewable resources and pave the way for a more sustainable future

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Artificial Neural Network and its Applications in the Energy Sector – An Overview

In order to realize the goal of optimal use of energy sources and cleaner environment at a minimal cost, researchers; field professionals; and industrialists have identified the expediency of harnessing the computational benefits provided by artificial intelligence (AI) techniques. This article provides an overview of AI, chronological blueprints of the emergence of artificial neural networks (ANNs) and some of its applications in the energy sector. This short survey reveals that despite the initial hiccups at the developmental stages of ANNs, ANN has tremendously evolved, is still evolving and have been found to be effective in handling highly complex problems even in the areas of modeling, control, and optimization, to mention a few

Future grid for a sustainable green airport: meeting the new loads of electric taxiing and electric aircraft.

Lao, Liyun - Associate SupervisorThis thesis proposes a novel electric grid in the airside to meet zero-emission targets for ground movement operations in future airports, as mandated by Aeronautics Research performance target in Europe's (ACARE) FlightPath 2050. The grid delivers power from a renewable energy source through a flexible powerline using an autonomous electric taxiing robot (A-ETR) based on the concept of Energy As A Service (EAAS) for taxiing large aircraft and charging stations for ground vehicles. Four layers of optimisation are required to realise the viability of this new grid. The first optimisation layer involves creating an analytical model of the A-ETR using real-world data from Cranfield University's Airport based solar PV system and its Boeing 737 research aircraft and optimising its performance and efficiency using vehicle-level data-driven machine learning- based optimisation. As a result, the proposed grid achieves zero-emission taxiing and a 91% reduction in fuel compared to a standard baseline. The second layer optimises energy management in the microgrid using machine learning-based forecasting models to predict PV output and optimise charging and discharging cycles of A-ETR batteries to match solar resources and electricity rates. The result shows that the support vector regression (SVR) model best predicted PV output and optimised BESS charge/discharge cycles to achieve zero-emission airport ground movement operations while reducing the microgrid operating costs. However, ground traffic and load profiles increase as the model expands to include commercial airports. Therefore, the third optimisation layer develops a machine learning-based data-driven energy prediction optimisation to ensure microgrid resilience under the increased load. The model employs the Facebook Prophet algorithm to enhance the precision of energy demand prediction for airport ground movement operations across three- time horizons. The results facilitate the generation of reliable forecasts for clean energy production and ground movement energy demand at the airport. A fourth layer of optimisation has been developed to address the limitations of solar PV energy, which depend on the weather and cannot be dispatched, as well iii as the increase in airport traffic. The layer uses wind power and data from a "green" airport to complement PV power output. This model uses the stochastic model predictive control-based cascade feedforward neural network (SMPC- CFFNN) to optimise power flow between the microgrid and RES sources and support V2G capabilities. The results demonstrate that a Zero-emission microgrid for ground movement at green airports can be achieved through optimal power flow management and time optimisation. Reliability and resilience are crucial for a proposed microgrid ecosystem. We consider different network configurations to connect the existing airport grid. Two microgrid architectures, LVAC and LVDC, are compared based on their point of common connections (PCC) to evaluate the technical and economic implications on the airport's distribution network. We verify and validate the model's performance in terms of power quality, short circuit fault levels, system protection requirements, voltage profile, power losses, and equipment/system overloading to determine the optimal architecture. The results indicate that the A-ETR can provide ancillary services to the grid and enable novel emergency response systems. The comprehensive results from the multi-layered system-level optimisation approach adopted in this thesis not only validate the novelty of the proposed study but also serve to provide compelling evidence for its potential to provide viable solutions to the electrification challenges for future green airports by creating an ecosystem between airport ground operations and on-site renewable energy generating sources.PhD in Energy and Powe

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Smart models to improve agrometeorological estimations and predictions

La población mundial, en continuo crecimiento, alcanzará de forma estimada los 9,7 mil millones de habitantes en el 2050. Este incremento, combinado con el aumento en los estándares de vida y la situación de emergencia climática (aumento de la temperatura, intensificación del ciclo del agua, etc.) nos enfrentan al enorme desafío de gestionar de forma sostenible los cada vez más escasos recursos disponibles. El sector agrícola tiene que afrontar retos tan importantes como la mejora en la gestión de los recursos naturales, la reducción de la degradación medioambiental o la seguridad alimentaria y nutricional. Todo ello condicionado por la escasez de agua y las condiciones de aridez: factores limitantes en la producción de cultivos. Para garantizar una producción agrícola sostenible bajo estas condiciones, es necesario que todas las decisiones que se tomen estén basadas en el conocimiento, la innovación y la digitalización de la agricultura de forma que se garantice la resiliencia de los agroecosistemas, especialmente en entornos áridos, semi-áridos y secos sub-húmedos en los que el déficit de agua es estructural. Por todo esto, el presente trabajo se centra en la mejora de la precisión de los actuales modelos agrometeorológicos, aplicando técnicas de inteligencia artificial. Estos modelos pueden proporcionar estimaciones y predicciones precisas de variables clave como la precipitación, la radiación solar y la evapotranspiración de referencia. A partir de ellas, es posible favorecer estrategias agrícolas más sostenibles, gracias a la posibilidad de reducir el consumo de agua y energía, por ejemplo. Además, se han reducido el número de mediciones requeridas como parámetros de entrada para estos modelos, haciéndolos más accesibles y aplicables en áreas rurales y países en desarrollo que no pueden permitirse el alto costo de la instalación, calibración y mantenimiento de estaciones meteorológicas automáticas completas. Este enfoque puede ayudar a proporcionar información valiosa a los técnicos, agricultores, gestores y responsables políticos de la planificación hídrica y agraria en zonas clave. Esta tesis doctoral ha desarrollado y validado nuevas metodologías basadas en inteligencia artificial que han ser vido para mejorar la precision de variables cruciales en al ámbito agrometeorológico: precipitación, radiación solar y evapotranspiración de referencia. En particular, se han modelado sistemas de predicción y rellenado de huecos de precipitación a diferentes escalas utilizando redes neuronales. También se han desarrollado modelos de estimación de radiación solar utilizando exclusivamente parámetros térmicos y validados en zonas con características climáticas similares a lugar de entrenamiento, sin necesidad de estar geográficamente en la misma región o país. Analógamente, se han desarrollado modelos de estimación y predicción de evapotranspiración de referencia a nivel local y regional utilizando también solamente datos de temperatura para todo el proceso: regionalización, entrenamiento y validación. Y finalmente, se ha creado una librería de Python de código abierto a nivel internacional (AgroML) que facilita el proceso de desarrollo y aplicación de modelos de inteligencia artificial, no solo enfocadas al sector agrometeorológico, sino también a cualquier modelo supervisado que mejore la toma de decisiones en otras áreas de interés.The world population, which is constantly growing, is estimated to reach 9.7 billion people in 2050. This increase, combined with the rise in living standards and the climate emergency situation (increase in temperature, intensification of the water cycle, etc.), presents us with the enormous challenge of managing increasingly scarce resources in a sustainable way. The agricultural sector must face important challenges such as improving natural resource management, reducing environmental degradation, and ensuring food and nutritional security. All of this is conditioned by water scarcity and aridity, limiting factors in crop production. To guarantee sustainable agricultural production under these conditions, it is necessary to based all the decision made on knowledge, innovation, and the digitization of agriculture to ensure the resilience of agroecosystems, especially in arid, semi-arid, and sub-humid dry environments where water deficit is structural. Therefore, this work focuses on improving the precision of current agrometeorological models by applying artificial intelligence techniques. These models can provide accurate estimates and predictions of key variables such as precipitation, solar radiation, and reference evapotranspiration. This way, it is possible to promote more sustainable agricultural strategies by reducing water and energy consumption, for example. In addition, the number of measurements required as input parameters for these models has been reduced, making them more accessible and applicable in rural areas and developing countries that cannot afford the high cost of installing, calibrating, and maintaining complete automatic weather stations. This approach can help provide valuable information to technicians, farmers, managers, and policy makers in key wáter and agricultural planning areas. This doctoral thesis has developed and validated new methodologies based on artificial intelligence that have been used to improve the precision of crucial variables in the agrometeorological field: precipitation, solar radiation, and reference evapotranspiration. Specifically, prediction systems and gap-filling models for precipitation at different scales have been modeled using neural networks. Models for estimating solar radiation using only thermal parameters have also been developed and validated in areas with similar climatic characteristics to the training location, without the need to be geographically in the same region or country. Similarly, models for estimating and predicting reference evapotranspiration at the local and regional level have been developed using only temperature data for the entire process: regionalization, training, and validation. Finally, an internationally open-source Python library (AgroML) has been created to facilitate the development and application of artificial intelligence models, not only focused on the agrometeorological sector but also on any supervised model that improves decision-making in other areas of interest

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

Neural Network Fault Recognition in Power Systems with High Penetrations of Inverter-Based Resources

The growing demand for renewable energy resources (RER) has led to increased integration of inverter-based resources (IBRs), into existing power distribution and transmission networks. However, RER locations are often not ideally suited for direct integration, necessitating a restructuring of the grid from a traditional radial network to a more complex mesh network topology. This transition presents challenges in terms of protection and coordination, as IBRs exhibit atypical responses to power system anomalies compared to conventional synchronous generation. To address these challenges and support existing power system protection infrastructure, this work explores the incorporation of machine learning algorithms. Specifically, an optimized convolutional neural network (CNN) is developed for real-time application in power system protection schemes. The focus is on prioritizing key performance metrics such as recall, specificity, speed, and the reduction of computational resources required for effective protection. The machine learning model is trained to differentiate between healthy system dynamics and hazardous conditions, such as faults, in the presence of IBRs. By analyzing data retrieved from an IEEE 34-bus 24kV distribution network, the model's application is demonstrated and its performance is evaluated. A photovoltaic (PV) source was incorporated into the IEEE 34-bus distribution feeder model at the end of the feeder. By adding a PV source at the end of the feeder, IBR characteristics, such as its response to system anomalies can be monitored through the model. Once the modified IEEE 34-bus distribution feeder model with the PV source was set up, various system anomalies were simulated to create a diverse dataset for training the machine learning (ML) model. These anomalies included; load rejection - a sudden and complete removal of load from the distribution network, simulating a scenario where a significant portion of the load disconnects from the grid, load addition - a sudden and significant increase in load demand, representing a scenario where new loads are connected to the grid, islanding - a scenario where the distribution feeder becomes electrically isolated from the main grid, with the PV source acting as a microgrid and supplying power to the local loads, and various types of faults, such as short-circuits or ground faults, occurring at different locations along the distribution line. To create a diverse dataset, model parameters were varied through 50 different iterations of each simulated anomaly scenario. These parameters included the PV system's capacity, the location of the anomaly on the feeder, the severity and duration of the anomaly, and other relevant grid parameters. For each iteration and anomaly scenario, the responses of the system were recorded, including voltage levels, current flows, and other relevant synchorphasors at the PV source's point of common coupling (PCC). These responses formed the dataset for training the ML model. The accumulated dataset was then used to train the various ML models, including the optimized convolutional neural network (CNN), to identify patterns and hidden characteristics in the data corresponding to different system anomalies. The training process involved feeding the model with input data from the various iterations and scenarios, along with corresponding labels indicating the type of anomaly present. By exposing the ML model to diverse scenarios and varying parameters, the model learns to generalize its understanding of system dynamics and accurately distinguish between healthy system states and hazardous conditions. The models in this work were specifically trained to recognize the various fault characteristics on the system. The trained model's ability to process time-series data and recognize anomalies from the accumulated dataset enhances power system protection infrastructure's capability to respond rapidly and accurately to various grid disturbances, ensuring the reliable and stable operation of the distribution network, especially in the presence of PV and other IBRs. The results show that the optimized CNN outperforms traditional machine learning models used in time-series data analysis. The model's speed and reliability make it an effective tool for identifying hidden characteristics in power system data without the need for extensive manual analysis or rigid programming of existing protection relays. This capability is particularly valuable as power grids integrate a higher penetration of IBRs, where traditional protection infrastructure may not fully account for their unique responses. The successful integration of the optimized CNN into power system protection infrastructure enhances the grid's ability to detect and respond to anomalies, such as faults, in a more efficient and accurate manner. By leveraging machine learning techniques, power system operators can better adapt to the challenges posed by the increasing presence of IBRs and ensure the continued stability and reliability of the distribution network