Particle swarm grammatical evolution for energy demand estimation

[EN] Grammatical Swarm is a search and optimization algorithm that belongs to the more general Grammatical Evolution family, which works with a set of solutions called individuals or particles. It uses the Particle Swarm Optimization algorithm as the search engine in the evolution of solutions. In this paper, we present a Grammatical Swarm algorithm for total energy demand estimation in a country from macroeconomic variables. Each particle in the Grammatical Swarm encodes a different model for energy demand estimation, which will be decoded by a predefined grammar. The parameters of the model are also optimized by the proposed algorithm, in such a way that the model is adjusted to a training set of real energy demand data, selecting the more appropriate variables to appear in the model. We analyze the performance of the Grammatical Swarm evolution in two real problems of one-year ahead energy demand estimation in Spain and France. The proposal is compared with previous approaches with competitive results.Spanish Ministerial Commission of Science and Technology (MICYT), Grant/Award Number: TIN2017-85887-C2-2-P; Ministerio de Ciencia, Innovacion y Universidades, Grant/Award Number: PGC2018-095322-B-C22 and RTI2018-095180-B-I00; Comunidad de Madrid y Fondos Estructurales de la Union Europea, Grant/Award Number: S2018/TCS-4566 and Y2018/NMT-4668; GenObIA-CM, Grant/Award Number: S2017/BMD-3773; Ministerio de Economia, Industria y Competitividad, Grant/Award Number: MTM2017-89664-PMartínez-Rodríguez, D.; Colmenar, JM.; Hidalgo, JI.; Villanueva Micó, RJ.; Salcedo-Sanz, S. (2020). Particle swarm grammatical evolution for energy demand estimation. Energy Science & Engineering. 8(4):1068-1079. https://doi.org/10.1002/ese3.568S1068107984Safarzyńska, K., & van den Bergh, J. C. J. M. (2017). Integrated crisis-energy policy: Macro-evolutionary modelling of technology, finance and energy interactions. Technological Forecasting and Social Change, 114, 119-137. doi:10.1016/j.techfore.2016.07.033Li, F., Song, Z., & Liu, W. (2014). China’s energy consumption under the global economic crisis: Decomposition and sectoral analysis. Energy Policy, 64, 193-202. doi:10.1016/j.enpol.2013.09.014Salcedo-Sanz, S., Muñoz-Bulnes, J., Portilla-Figueras, J. A., & Del Ser, J. (2015). One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms. Energy Conversion and Management, 99, 62-71. doi:10.1016/j.enconman.2015.03.109Sánchez-Oro, J., Duarte, A., & Salcedo-Sanz, S. (2016). Robust total energy demand estimation with a hybrid Variable Neighborhood Search – Extreme Learning Machine algorithm. Energy Conversion and Management, 123, 445-452. doi:10.1016/j.enconman.2016.06.050Ceylan, H., & Ozturk, H. K. (2004). Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion and Management, 45(15-16), 2525-2537. doi:10.1016/j.enconman.2003.11.010Shaik, S., & Yeboah, O.-A. (2018). Does climate influence energy demand? A regional analysis. Applied Energy, 212, 691-703. doi:10.1016/j.apenergy.2017.11.109United Nations Climate Change Conference.The Paris Agreement. UNTC XXVII 7.d.Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and Sustainable Energy Reviews, 16(2), 1223-1240. doi:10.1016/j.rser.2011.08.014Geem, Z. W., & Roper, W. E. (2009). Energy demand estimation of South Korea using artificial neural network. Energy Policy, 37(10), 4049-4054. doi:10.1016/j.enpol.2009.04.049Ünler, A. (2008). Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025. Energy Policy, 36(6), 1937-1944. doi:10.1016/j.enpol.2008.02.018Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). Swarm intelligence approaches to estimate electricity energy demand in Turkey. Knowledge-Based Systems, 36, 93-103. doi:10.1016/j.knosys.2012.06.009Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey. Energy Conversion and Management, 53(1), 75-83. doi:10.1016/j.enconman.2011.08.004Yu, S., Wei, Y.-M., & Wang, K. (2012). A PSO–GA optimal model to estimate primary energy demand of China. Energy Policy, 42, 329-340. doi:10.1016/j.enpol.2011.11.090Yu, S., Zhu, K., & Zhang, X. (2012). Energy demand projection of China using a path-coefficient analysis and PSO–GA approach. Energy Conversion and Management, 53(1), 142-153. doi:10.1016/j.enconman.2011.08.015Yu, S., & Zhu, K. (2012). A hybrid procedure for energy demand forecasting in China. Energy, 37(1), 396-404. doi:10.1016/j.energy.2011.11.015Geng, Z., Zeng, R., Han, Y., Zhong, Y., & Fu, H. (2019). Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries. Energy, 179, 863-875. doi:10.1016/j.energy.2019.05.042Han, Y., Long, C., Geng, Z., Zhu, Q., & Zhong, Y. (2019). A novel DEACM integrating affinity propagation for performance evaluation and energy optimization modeling: Application to complex petrochemical industries. Energy Conversion and Management, 183, 349-359. doi:10.1016/j.enconman.2018.12.120Han, Y., Wu, H., Jia, M., Geng, Z., & Zhong, Y. (2019). Production capacity analysis and energy optimization of complex petrochemical industries using novel extreme learning machine integrating affinity propagation. Energy Conversion and Management, 180, 240-249. doi:10.1016/j.enconman.2018.11.001Colmenar, J. M., Hidalgo, J. I., & Salcedo-Sanz, S. (2018). Automatic generation of models for energy demand estimation using Grammatical Evolution. Energy, 164, 183-193. doi:10.1016/j.energy.2018.08.199O’Neill, M., & Brabazon, A. (2006). Grammatical Swarm: The generation of programs by social programming. Natural Computing, 5(4), 443-462. doi:10.1007/s11047-006-9007-7O’Neill, M., & Ryan, C. (2001). Grammatical evolution. IEEE Transactions on Evolutionary Computation, 5(4), 349-358. doi:10.1109/4235.942529KennedyJ EberhartR.Particle swarm optimization. Vol.4. Proceedings of ICNN'95 - International Conference on Neural Networks.Perth WA;1995:1942‐1948.https://doi.org/10.1109/ICNN.1995.488968Tsoulos, I. G., Gavrilis, D., & Glavas, E. (s. f.). Neural network construction using grammatical evolution. Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005. doi:10.1109/isspit.2005.1577206Beni, G., & Wang, J. (1993). Swarm Intelligence in Cellular Robotic Systems. Robots and Biological Systems: Towards a New Bionics?, 703-712. doi:10.1007/978-3-642-58069-7_38Krause, J., Ruxton, G. D., & Krause, S. (2010). Swarm intelligence in animals and humans. Trends in Ecology & Evolution, 25(1), 28-34. doi:10.1016/j.tree.2009.06.016Marini, F., & Walczak, B. (2015). Particle swarm optimization (PSO). A tutorial. Chemometrics and Intelligent Laboratory Systems, 149, 153-165. doi:10.1016/j.chemolab.2015.08.020Ling, C. X. (1995). Overfitting and generalization in learning discrete patterns. Neurocomputing, 8(3), 341-347. doi:10.1016/0925-2312(95)00050-

Forecasting methods in energy planning models

Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs

Feature Papers of Forecasting 2021

This book focuses on fundamental and applied research on forecasting methods and analyses on how forecasting can affect a great number of fields, spanning from Computer Science, Engineering, and Economics and Business to natural sciences. Forecasting applications are increasingly important because they allow for improving decision-making processes by providing useful insights about the future. Scientific research is giving unprecedented attention to forecasting applications, with a continuously growing number of articles about novel forecast approaches being publishe

Feature Papers of Forecasting 2021

Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND

Nuevos algoritmos de soft-computing en física atmosférica

Tesis de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, leída el 12-03-2019This Ph.D. Thesis elaborates and analyzes several hybrid Soft-Computing algorithms for optimization and prediction problems in Atmospheric Physics. The core of the Thesis is a recently developed optimization meta-heuristic, the Coral Reefs Optimization Algorithm (CRO), an evolutionary-based approach which considers a population of possible solutions to a given optimization problem. It simulates different procedures mimicking real processes occurring in coral reefs in order to evolve the population towards good solutions for the problem. Alternative modifications of this algorithm lead to powerful co-evolution meta-heuristics, such as theCRO-SL, in which Substrates implementing different search procedures are included. Another modification of the algorithm leads to the CRO-SP, which considers Species in the evolutionof the population, and it is able to deal with different encodings within a single population.These approaches are hybridized with other Machine Learning and traditional algorithms such as neural networks or the Analogue Method (AM), to come up with powerful hybrid approaches able to solve hard problems in Atmospheric Physics...En esta Tesis Doctoral se elaboran y analizan en detalle diferentes algoritmos híbridos deSoft-Computing para problemas de optimización y predicción en Física de la Atmósfera. El núcleo central de la Tesis es un algoritmo meta-heurístico de optimización recientemente desarrollado, conocido como Coral Reefs Optimization algorithm (CRO). Este algoritmo pertenece a la familia de la Computación Evolutiva, de forma que considera una población de solucionesa un problema concreto, y simula los diferentes procesos que ocurren en un arrecife de coralpara evolucionar dicha población hacia la solución óptima del problema. Recientemente se han propuesto diferentes versiones del algoritmo CRO básico para obtener mecanismos potentes de optimización co-evolutiva. Una de estas modificaciones es el CRO-SL, en la que se definen un conjunto de Sustratos en el algoritmo, de manera que cada sustrato simula un mecanismo de evolución diferente, que son aplicados a la vez en una única población. Otra modificación hadado lugar al conocido como CRO-SP, un algoritmo donde se definen diferentes Especies, capaz de manejar varias codificaciones para un mismo problema a la vez. Estas versiones del CRO han sido hibridadas con varias técnicas de Aprendizaje Máquina, tales como varios tipos de redes neuronales de entrenamiento rápido, sistemas de aprendizaje tales como Máquinas de Vectores Soporte, o sistemas de predicción vinculados totalmente al área de la Física Atmosférica, tales como el Método de los Análogos (AM). Los algoritmos híbridos obtenidos son muy robustos y capaces de obtener excelentes soluciones en diferentes problemas donde han sido probados...Fac. de Ciencias FísicasTRUEunpu

Optimization Algorithm Based on Novelty Search Applied to the Treatment of Uncertainty in Models

[ES] La búsqueda novedosa es un nuevo paradigma de los algoritmos de optimización, evolucionarios y bioinspirados, que está basado en la idea de forzar la búsqueda del óptimo global en aquellas partes inexploradas del dominio de la función que no son atractivas para el algoritmo, con la intención de evitar estancamientos en óptimos locales. La búsqueda novedosa se ha aplicado al algoritmo de optimización de enjambre de partículas, obteniendo un nuevo algoritmo denominado algoritmo de enjambre novedoso (NS). NS se ha aplicado al conjunto de pruebas sintéticas CEC2005, comparando los resultados con los obtenidos por otros algoritmos del estado del arte. Los resultados muestran un mejor comportamiento de NS en funciones altamente no lineales, a cambio de un aumento en la complejidad computacional. En lo que resta de trabajo, el algoritmo NS se ha aplicado en diferentes modelos, específicamente en el diseño de un motor de combustión interna, en la estimación de demanda de energía mediante gramáticas de enjambre, en la evolución del cáncer de vejiga de un paciente concreto y en la evolución del COVID-19. Cabe remarcar que, en el estudio de los modelos de COVID-19, se ha tenido en cuenta la incertidumbre, tanto de los datos como de la evolución de la enfermedad.[CA] La cerca nova és un nou paradigma dels algoritmes d'optimització, evolucionaris i bioinspirats, que està basat en la idea de forçar la cerca de l'òptim global en les parts inexplorades del domini de la funció que no són atractives per a l'algoritme, amb la intenció d'evitar estancaments en òptims locals. La cerca nova s'ha aplicat a l'algoritme d'optimització d'eixam de partícules, obtenint un nou algoritme denominat algoritme d'eixam nou (NS). NS s'ha aplicat al conjunt de proves sintètiques CEC2005, comparant els resultats amb els obtinguts per altres algoritmes de l'estat de l'art. Els resultats mostren un millor comportament de NS en funcions altament no lineals, a canvi d'un augment en la complexitat computacional. En el que resta de treball, l'algoritme NS s'ha aplicat en diferents models, específicament en el disseny d'un motor de combustió interna, en l'estimació de demanda d'energia mitjançant gramàtiques d'eixam, en l'evolució del càncer de bufeta d'un pacient concret i en l'evolució del COVID-19. Cal remarcar que, en l'estudi dels models de COVID-19, s'ha tingut en compte la incertesa, tant de les dades com de l'evolució de la malaltia.[EN] Novelty Search is a recent paradigm in evolutionary and bio-inspired optimization algorithms, based on the idea of forcing to look for those unexplored parts of the domain of the function that might be unattractive for the algorithm, with the aim of avoiding stagnation in local optima. Novelty Search has been applied to the Particle Swarm Optimization algorithm, obtaining a new algorithm named Novelty Swarm (NS). NS has been applied to the CEC2005 benchmark, comparing its results with other state of the art algorithms. The results show better behaviour in high nonlinear functions at the cost of increasing the computational complexity. During the rest of the thesis, the NS algorithm has been used in different models, specifically the design of an Internal Combustion Engine, the prediction of energy demand estimation with Grammatical Swarm, the evolution of the bladder cancer of a specific patient and the evolution of COVID-19. It is also remarkable that, in the study of COVID-19 models, uncertainty of the data and the evolution of the disease has been taken in account.Martínez Rodríguez, D. (2021). Optimization Algorithm Based on Novelty Search Applied to the Treatment of Uncertainty in Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17899