Short-term forecasting photovoltaic solar power for home energy management systems

Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020 grant 01/SAICT/2018. Antonio Ruano acknowledges the support of Fundação para a Ciência e Tecnologia, through IDMEC, under LAETA, grant UIDB/50022/2020.info:eu-repo/semantics/publishedVersio

HVAC-based hierarchical energy management system for microgrids

With the high penetration of renewable energy into the grid, power fluctuations and supply-demand power mismatch are becoming more prominent, which pose a great challenge for the power system to eliminate negative effects through demand side management (DSM). The flexible load, such as heating, ventilation, air conditioning (HVAC) system, has a great potential to provide demand response services in the electricity grids. In this thesis, a comprehensive framework based on a forecasting-management optimization approach is proposed to coordinate multiple HVAC systems to deal with uncertainties from renewable energy resources and maximize the energy efficiency. In the forecasting stage, a hybrid model based on Multiple Aggregation Prediction Algorithm with exogenous variables (MAPAx)-Principal Components Analysis (PCA) is proposed to predict changes of local solar radiance, vy using the local observation dataset and real-time meteorological indexes acquired from the weather forecast spot. The forecast result is then compared with the statistical benchmark models and assessed by performance evaluation indexes. In the management stage, a novel distributed algorithm is developed to coordinate power consumption of HVAC systems by varying the compressors’ frequency to maintain the supply-demand balance. It demonstrates that the cost and capacity of energy storage systems can be curtailed, since HVACs can absorb excessive power generation. More importantly, the method addresses a consensus problem under a switching communication topology by using Lyapunov argument, which relaxes the communication requirement. In the optimization stage, a price-comfort optimization model regarding HVAC’s end users is formulated and a proportional-integral-derivative (PID)-based distributed algorithm is thus developed to minimize the customer’s total cost, whilst alleviating the global power imbalance. The end users are motivated to participate in energy trade through DSM scheme. Furthermore, the coordination scheme can be extended to accommodate battery energy storage systems (BESSs) and a hybrid BESS-HVAC system with increasing storage capacity is proved as a promising solution to enhance its selfregulation ability in a microgrid. Extensive case studies have been undertaken with the respective control strategies to investigate effectiveness of the algorithms under various scenarios. The techniques developed in this thesis has helped the partnership company of this project to develop their smart immersion heaters for the customers with minimum energy cost and maximum photovoltaic efficiency

Computational intelligence techniques for maritime and coastal remote sensing

The aim of this thesis is to investigate the potential of computational intelligence techniques for some applications in the analysis of remotely sensed multi-spectral images. In particular, two problems are addressed. The first one is the classification of oil spills at sea, while the second one is the estimation of sea bottom depth. In both cases, the exploitation of optical satellite data allows to develop operational tools for easily accessing and monitoring large marine areas, in an efficient and cost effective way. Regarding the oil spill problem, today public opinion is certainly aware of the huge impact that oil tanker accidents and oil rig leaks have on marine and coastal environment. However, it is less known that most of the oil released in our seas cannot be ascribed to accidental spills, but rather to illegal ballast waters discharge, and to pollutant dumping at sea, during routine operations of oil tankers. For this reason, any effort for improving oil spill detection systems is of great importance. So far, Synthetic Aperture Radar (SAR) data have been preferred to multi-spectral data for oil spill detection applications, because of their all-weather and all-day capabilities, while optical images necessitate of clear sky conditions and day-light. On the other hand, many features make an optical approach desirable, such as lower cost and higher revisit time. Moreover, unlike SAR data, optical data are not affected by sea state, and are able to reduce false alarm rate, since they do not suffer from the main false alarm source in SAR data, that is represented by the presence of calm sea regions. In this thesis the problem of oil spill classification is tackled by applying different machine learning techniques to a significant dataset of regions of interest, collected in multi-spectral satellite images, acquired by MODIS sensor. These regions are then classified in one of two possible classes, that are oil spills and look-alikes, where look-alikes include any phenomena other than oil spills (e.g. algal blooms...). Results show that efficient and reliable oil spill classification systems based on optical data are feasible, and could offer a valuable support to the existing satellite-based monitoring systems. The estimation of sea bottom depth from high resolution multi-spectral satellite images is the second major topic of this thesis. The motivations for dealing with this problem arise from the necessity of limiting expensive and time consuming measurement campaigns. Since satellite data allow to quickly analyse large areas, a solution for this issue is to employ intelligent techniques, which, by exploiting a small set of depth measurements, are able to extend bathymetry estimate to a much larger area, covered by a multi-spectral satellite image. Such techniques, once that the training phase has been completed, allow to achieve very accurate results, and, thanks to their generalization capabilities, provide reliable bathymetric maps which cover wide areas. A crucial element is represented by the training dataset, which is built by coupling a number of depth measurements, located in a limited part of the image, with corresponding radiances, acquired by the satellite sensor. A successful estimate essentially depends on how the training dataset resembles the rest of the scene. On the other hand, the result is not affected by model uncertainties and systematic errors, as results from model-based analytic approaches are. In this thesis a neuro-fuzzy technique is applied to two case studies, more precisely, two high resolution multi-spectral images related to the same area, but acquired in different years and in different meteorological conditions. Different situations of in-situ depths availability are considered in the study, and the effect of limited in-situ data availability on performance is evaluated. The effect of both meteorological conditions and training set size reduction on the overall performance is also taken into account. Results outperform previous studies on bathymetry estimation techniques, and allow to give indications on the optimal paths which can be adopted when planning data collection at sea

An assessment of the load modifying potential of model predictive controlled dynamic facades within the California context

California is making major strides towards meeting its greenhouse gas emission reduction goals with the transformation of its electrical grid to accommodate renewable generation, aggressive promotion of building energy efficiency, and increased emphasis on moving toward electrification of end uses (e.g., residential heating, etc.). As a result of this activity, the State is faced with significant challenges of systemwide resource adequacy, power quality and grid reliability that could be addressed in part with demand responsive (DR) load modifying strategies using controllable building technologies. Dynamic facades have the ability to potentially shift and shed loads at critical times of the day in combination with daylighting and HVAC controls. This study explores the technical potential of dynamic facades to support net load shape objectives. A model predictive controller (MPC) was designed based on reduced order thermal (Modelica) and window (Radiance) models. Using an automated workflow (involving JModelica.org and MPCPy), these models were converted and differentiated to formulate a non-linear optimization problem. A gradient-based, non-linear programming problem solver (IPOPT) was used to derive an optimal control strategy, then a post-optimization step was used to convert the solution to a discrete state for facade actuation. Continuous state modulation of the façade was also modeled. The performance of the MPC controller with and without activation of thermal mass was evaluated in a south-facing perimeter office zone with a three-zone electrochromic window for a clear sunny week during summer and winter periods in Oakland and Burbank, California. MPC strategies reduced total energy cost by 9–28% and critical coincident peak demand was reduced by up to 0.58 W/ft2-floor or 19–43% in the 4.6 m (15 ft) deep south zone on sunny summer days in Oakland compared to state-of-the-art heuristic control. Similar savings were achieved for the hotter, Burbank climate in Southern California. This outcome supports the argument that MPC control of dynamic facades can provide significant electricity cost reductions and net load management capabilities of benefit to both the building owner and evolving electrical grid

Combining point cloud and surface methods for modeling partial shading impacts of trees on urban solar irradiance

Although trees and urban vegetation have a significant influence on solar irradiation in the built environment, their impact on daylight and energy consumption is often not considered in building performance and urban environment simulation studies. This paper presents a novel method for comprehensive solar irradiance assessment that considers the dynamic partial shading impacts from trees. The proposed method takes urban point clouds as input and consists of three subsequent steps: (a) DGCNN-based segmentation, (b) fusion model generation, (c) matrix-based irradiance calculation. The method is validated by comparing model outputs with field measurement data, and an inter-model comparison with eleven state-of-the-art tree shading modeling approaches. Analyses carried out on daily and long-term basis show that the proposed fusion model can significantly reduce simulation errors compared to alternative approaches, while limiting the required input data to a minimum. The primary source of uncertainty stems from mismatches between tree morphology in the fusion model and reality, attributable to phenological growth and seasonal variations.</p

Model Predictive Control Based on Deep Learning for Solar Parabolic-Trough Plants

En la actualidad, cada vez es mayor el interés por utilizar energías renovables, entre las que se encuentra la energía solar. Las plantas de colectores cilindro-parabólicos son un tipo de planta termosolar donde se hace incidir la radiación del Sol en unos tubos mediante el uso de unos espejos con forma de parábola. En el interior de estos tubos circula un fluido, generalmente aceite o agua, que se calienta para generar vapor y hacer girar una turbina, produciendo energía eléctrica. Uno de los métodos más utilizados para manejar estas plantas es el control predictivo basado en modelo (model predictive control, MPC), cuyo funcionamiento consiste en obtener las señales de control óptimas que se enviarán a la planta basándose en el uso de un modelo de la misma. Este método permite predecir el estado que adoptará el sistema según la estrategia de control escogida a lo largo de un horizonte de tiempo. El MPC tiene como desventaja un gran coste computacional asociado a la resolución de un problema de optimización en cada instante de muestreo. Esto dificulta su implementación en plantas comerciales y de gran tamaño, por lo que, actualmente, uno de los principales retos es la disminución de estos tiempos de cálculo, ya sea tecnológicamente o mediante el uso de técnicas subóptimas que simplifiquen el problema. En este proyecto, se propone el uso de redes neuronales que aprendan offline de la salida proporcionada por un controlador predictivo para luego poder aproximarla. Se han entrenado diferentes redes neuronales utilizando un conjunto de datos de 30 días de simulación y modificando el número de entradas. Los resultados muestran que las redes neuronales son capaces de proporcionar prácticamente la misma potencia que el MPC con variaciones más suaves de la salida y muy bajas violaciones de las restricciones, incluso disminuyendo el número de entradas. El trabajo desarrollado se ha publicado en Renewable Energy, una revista del primer cuartil en Green & sustainable science & technology y Energy and fuels.Nowadays, there is an increasing interest in using renewable energy sources, including solar energy. Parabolic trough plants are a type of solar thermal power plant in which solar radiation is reflected onto tubes with parabolic mirrors. Inside these tubes circulates a fluid, usually oil or water, which is heated to generate steam and turn a turbine to produce electricity. One of the most widely used methods to control these plants is model predictive control (MPC), which obtains the optimal control signals to send to the plant based on the use of a model. This method makes it possible to predict its future state according to the chosen control strategy over a time horizon. The MPC has the disadvantage of a significant computational cost associated with resolving an optimization problem at each sampling time. This makes it challenging to implement in commercial and large plants, so currently, one of the main challenges is to reduce these computational times, either technologically or by using suboptimal techniques that simplify the problem. This project proposes the use of neural networks that learn offline from the output provided by a predictive controller to then approximate it. Different neural networks have been trained using a 30-day simulation dataset and modifying the number of irradiance and temperature inputs. The results show that the neural networks can provide practically the same power as the MPC with smoother variations of the output and very low violations of the constraints, even when decreasing the number of inputs. The work has been published in Renewable Energy, a first quartile journal in Green & sustainable science & technology and Energy and fuels.Universidad de Sevilla. Máster en Ingeniería Industria

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems’ persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)