Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

Energy Consumption Forecasting Using Ensemble Learning Algorithms

DCAI 2019: Distributed Computing and Artificial Intelligence, 16th International Conference, Special SessionsThe increase of renewable energy sources of intermittent nature has brought several new challenges for power and energy systems. In order to deal with the variability from the generation side, there is the need to balance it by managing consumption appropriately. Forecasting energy consumption becomes, therefore, more relevant than ever. This paper presents and compares three different ensemble learning methods, namely random forests, gradient boosted regression trees and Adaboost. Hour-ahead electricity load forecasts are presented for the building N of GECAD at ISEP campus. The performance of the forecasting models is assessed, and results show that the Adaboost model is superior to the other considered models for the one-hour ahead forecasts. The results of this study compared to previous works indicates that ensemble learning methods are a viable choice for short-term load forecast.This work has received funding from National Funds through FCT (Fundaçao da Ciencia e Tecnologia) under the project SPET – 29165, call SAICT 2017.info:eu-repo/semantics/publishedVersio

State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems

The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon

Modeling of electricity demand forecast for power system

© 2019, Springer-Verlag London Ltd., part of Springer Nature. The emerging complex circumstances caused by economy, technology, and government policy and the requirement of low-carbon development of power grid lead to many challenges in the power system coordination and operation. However, the real-time scheduling of electricity generation needs accurate modeling of electricity demand forecasting for a range of lead times. In order to better capture the nonlinear and non-stationary characteristics and the seasonal cycles of future electricity demand data, a new concept of the integrated model is developed and successfully applied to research the forecast of electricity demand in this paper. The proposed model combines adaptive Fourier decomposition method, a new signal preprocessing technology, for extracting useful element from the original electricity demand series through filtering the noise factors. Considering the seasonal term existing in the decomposed series, it should be eliminated through the seasonal adjustment method, in which the seasonal indexes are calculated and should multiply the forecasts back to restore the final forecast. Besides, a newly proposed moth-flame optimization algorithm is used to ensure the suitable parameters of the least square support vector machine which can generate the forecasts. Finally, the case studies of Australia demonstrated the efficacy and feasibility of the proposed integrated model. Simultaneously, it can provide a better concept of modeling for electricity demand prediction over different forecasting horizons

A Survey on Data Mining Techniques Applied to Energy Time Series Forecasting

Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.Ministerio de Economía y Competitividad TIN2014-55894-C2-RJunta de Andalucía P12- TIC-1728Universidad Pablo de Olavide APPB81309

A novel soft computing approach based on FIR to model and predict energy dynamic systems

Tesi en modalitat compendi de publicacionsWe are facing a global climate crisis that is demanding a change in the status quo of how we produce, distribute and consume energy. In the last decades, this is being redefined through Smart Grids(SG), an intelligent electrical network more observable, controllable, automated, fully integrated with energy services and the end-users. Most of the features and proposed SG scenarios are based on reliable, robust and fast energy predictions. For instance, for proper planning activities, such as generation, purchasing, maintenance and investment; for demand side management, like demand response programs; for energy trading, especially at local level, where productions and consumptions are more stochastics and dynamic; better forecasts also increase grid stability and thus supply security. A large variety of Artificial Intelligence(AI) techniques have been applied in the field of Short-term electricity Load Forecasting(SLF) at consumer level in low-voltage system, showing a better performance than classical techniques. Inaccuracy or failure in the SLF process may be translated not just in a non-optimal (low prediction accuracy) solution but also in frustration of end-users, especially in new services and functionalities that empower citizens. In this regard, some limitations have been observed in energy forecasting models based on AI such as robustness, reliability, accuracy and computation in the edge. This research proposes and develops a new version of Fuzzy Inductive Reasoning(FIR), called Flexible FIR, to model and predict the electricity consumption of an entity in the low-voltage grid with high uncertainties, and information missing, as well as the capacity to be deployed either in the cloud or locally in a new version of Smart Meters(SMs) based on Edge Computing(EC). FIR has been proved to be a powerful approach for model identification and system ’s prediction over dynamic and complex processes in different real world domains but not yet in the energy domain. Thus, the main goal of this thesis is to demonstrate that a new version of FIR, more robust, reliable and accurate can be a referent Soft Computing(SC) methodology to model and predict dynamic systems in the energy domain and that it is scalable to an EC integration. The core developments of Flexible FIR have been an algorithm that can cope with missing information in the input values, as well as learn from instances with Missing Values(MVs) in the knowledge-based, without compromising significantly the accuracy of the predictions. Moreover, Flexible FIR comes with new forecasting strategies that can cope better with loss of causality of a variable and dispersion of output classes than classical k nearest neighbours, making the FIR forecasting process more reliable and robust. Furthermore, Flexible FIR addresses another major challenge modelling with SC techniques, which is to select best model parameters. One of the most important parameters in FIR is the number k of nearest neighbours to be used in the forecast process. The challenge to select the optimal k, dynamically, is addressed through an algorithm, called KOS(K nearest neighbour Optimal Selection), which has been developed and tested also with real world data. It computes a membership aggregation function of all the neighbours with respect their belonging to the output classes.While with KOS the optimal parameter k is found online, with other approaches such as genetic algorithms or reinforcement learning is not, which increases the computational time.Ens trobem davant una crisis climàtica global que exigeix un canvi al status quo de la manera que produïm, distribuïm i consumim energia. En les darreres dècades, està sent redefinit gràcies a les xarxa elèctriques intel·ligents(SG: Smart Grid) amb millor observabilitat, control, automatització, integrades amb nous serveis energètics i usuaris finals. La majoria de les funcionalitats i escenaris de les SG es basen en prediccions de la càrrega elèctrica confiables, robustes i ràpides. Per les prediccions de càrregues elèctriques a curt termini(SLF: Short-term electricity Load Forecasting), a nivell de consumidors al baix voltatge, s’han aplicat una gran varietat de tècniques intel·ligència Artificial(IA) mostrant millor rendiment que tècniques estadístiques tradicionals. Un baix rendiment en SLF, pot traduir-se no només en una solució no-òptima (baixa precisió de predicció) sinó també en la frustració dels usuaris finals, especialment en nous serveis i funcionalitats que empoderarien als ciutadans. En el marc d’aquesta investigació es proposa i desenvolupa una nova versió de la metodologia del Raonament Inductiu Difús(FIR: Fuzzy Inductive Reasoning), anomenat Flexible FIR, capaç de modelar i predir el consum d’electricitat d’una entitat amb un grau d’incertesa molt elevat, inclús amb importants carències d’informació (missing values). A més, Flexible FIR té la capacitat de desplegar-se al núvol, així como localment, en el que podria ser una nova versió de Smart Meters (SM) basada en tecnologia d’Edge Computing (EC). FIR ja ha demostrat ser una metodologia molt potent per la generació de models i prediccions en processos dinàmics en diferents àmbits, però encara no en el de l’energia. Per tant, l’objectiu principal d’aquesta tesis és demostrar que una versió millorada de FIR, més robusta, fiable i precisa pot consolidar-se com una metodologia Soft Computing SC) de referencia per modelar i predir sistemes dinàmics en aplicacions per al sector de l’energia i que és escalable a una integració d’EC. Les principals millores de Flexible FIR han estat, en primer lloc, el desenvolupament i test d’un algorisme capaç de processar els valors d’entrada d’un model FIR tot i que continguin Missing Values (MV). Addicionalment, aquest algorisme també permet aprendre d’instàncies amb MV en la matriu de coneixement d’un model FIR, sense comprometre de manera significativa la precisió de les prediccions. En segon lloc, s’han desenvolupat i testat noves estratègies per a la fase de predicció, comportant-se millor que els clàssics k veïns més propers quan ens trobem amb pèrdua de causalitat d’una variable i dispersió en les classes de sortida, aconseguint un procés d’aprenentatge i predicció més confiable i robust. En tercer lloc, Flexible FIR aborda un repte molt comú en tècniques de SC: l’òptima parametrització del model. En FIR, un dels paràmetres més determinants és el número k de veïns més propers que s’utilitzaran durant la fase de predicció. La selecció del millor valor de k es planteja de manera dinàmica a través de l’algorisme KOS (K nearest neighbour Optimal Selection) que s’ha desenvolupat i testat també amb dades reals. Mentre que amb KOS el paràmetre òptim de k es calcula online, altres enfocaments mitjançant algoritmes genètics o aprenentatge per reforç el càlcul és offline, incrementant significativament el temps de resposta, sent a més a més difícil la implantació en escenaris d’EC. Aquestes millores fan que Flexible FIR es pugui adaptar molt bé en aplicacions d’EC. En aquest sentit es proposa el concepte d’un SM de segona generació basat en EC, que integra Flexible FIR com mòdul de predicció d’electricitat executant-se en el propi dispositiu i un agent EC amb capacitat per el trading d'energia produïda localment. Aquest agent executa un innovador mecanisme basat en incentius, anomenat NRG-X-Change que utilitza una nova moneda digital descentralitzada per l’intercanvi d’energia, que s’anomena NRGcoin.Estamos ante una crisis climática global que exige un cambio del status quo de la manera que producimos, distribuimos y consumimos energía. En las últimas décadas, este status quo está siendo redefinido debido a: la penetración de las energías renovables y la generación distribuida; nuevas tecnologías como baterías y paneles solares con altos rendimientos; y la forma en que se consume la energía, por ejemplo, a través de vehículos eléctricos o con la electrificación de los hogares. Estas palancas requieren una red eléctrica inteligente (SG: Smart Grid) con mayor observabilidad, control, automatización y que esté totalmente integrada con nuevos servicios energéticos, así como con sus usuarios finales. La mayoría de las funcionalidades y escenarios de las redes eléctricas inteligentes se basan en predicciones de la energía confiables, robustas y rápidas. Por ejemplo, para actividades de planificación como la generación, compra, mantenimiento e inversión; para la gestión de la demanda, como los programas de demand response; en el trading de electricidad, especialmente a nivel local, donde las producciones y los consumos son más estocásticos y dinámicos; una mejor predicción eléctrica también aumenta la estabilidad de la red y, por lo tanto, mejora la seguridad. Para las predicciones eléctricas a corto plazo (SLF: Short-term electricity Load Forecasting), a nivel de consumidores en el bajo voltaje, se han aplicado una gran variedad de técnicas de Inteligencia Artificial (IA) mostrando mejor rendimiento que técnicas estadísticas convencionales. Un bajo rendimiento en los modelos predictivos, puede traducirse no solamente en una solución no-óptima (baja precisión de predicción) sino también en frustración de los usuarios finales, especialmente en nuevos servicios y funcionalidades que empoderan a los ciudadanos. En este sentido, se han identificado limitaciones en modelos de predicción de energía basados en IA, como la robustez, fiabilidad, precisión i computación en el borde. En el marco de esta investigación se propone y desarrolla una nueva versión de la metodología de Razonamiento Inductivo Difuso (FIR: Fuzzy Inductive Reasoning), que hemos llamado Flexible FIR, capaz de modelar y predecir el consumo de electricidad de una entidad con altos grados de incertidumbre e incluso con importantes carencias de información (missing values). Además, Flexible FIR tiene la capacidad de desplegarse en la nube, así como localmente, en lo que podría ser una nueva versión de Smart Meters (SM) basada en tecnología de Edge Computing (EC). En el pasado, ya se ha demostrado que FIR es una metodología muy potente para la generación de modelos y predicciones en procesos dinámicos, sin embargo, todavía no ha sido demostrado en el campo de la energía. Por tanto, el objetivo principal de esta tesis es demostrar que una versión mejorada de FIR, más robusta, fiable y precisa puede consolidarse como metodología Soft Computing (SC) de referencia para modelar y predecir sistemas dinámicos en aplicaciones para el sector de la energía y que es escalable hacia una integración de EC. Las principales mejoras en Flexible FIR han sido, en primer lugar, el desarrollo y testeo de un algoritmo capaz de procesar los valores de entrada en un modelo FIR a pesar de que contengan Missing Values (MV). Además, dicho algoritmo también permite aprender de instancias con MV en la matriz de conocimiento de un modelo FIR, sin comprometer de manera significativa la precisión de las predicciones. En segundo lugar, se han desarrollado y testeado nuevas estrategias para la fase de predicción de un modelo FIR, comportándose mejor que los clásicos k vecinos más cercanos ante la pérdida de causalidad de una variable y dispersión de clases de salida, consiguiendo un proceso de aprendizaje y predicción más confiable y robusto. En tercer lugar, Flexible FIR aborda un desafío muy común en técnicas de SC: la óptima parametrización del modelo. En FIR, uno de los parámetros más determinantes es el número k de vecinos más cercanos que se utilizarán en la fase de predicción. La selección del mejor valor de k se plantea de manera dinámica a través del algoritmo KOS (K nearest neighbour Optimal Selection) que se ha desarrollado y probado también con datos reales. Dicho algoritmo calcula una función de membresía agregada, de todos los vecinos, con respecto a su pertenencia a las clases de salida. Mientras que con KOS el parámetro óptimo de k se calcula online, otros enfoques mediante algoritmos genéticos o aprendizaje por refuerzo, el cálculo es offline incrementando significativamente el tiempo de respuesta, siendo además difícil su implantación en escenarios de EC. Estas mejoras hacen que Flexible FIR se adapte muy bien en aplicaciones de EC, en las que la analítica de datos en streaming debe ser fiable, robusta y con un modelo suficientemente ligero para ser ejecutado en un IoT Gateway o dispositivos más pequeños. También, en escenarios con poca conectividad donde el uso de la computación en la nube es limitado y los parámetros del modelo se calculan localmente. Con estas premisas, en esta tesis, se propone el concepto de un SM de segunda generación basado en EC, que integra Flexible FIR como módulo de predicción de electricidad ejecutándose en el dispositivo y un agente EC con capacidad para el trading de energía producida localmente. Dicho agente ejecuta un novedoso mecanismo basado en incentivos, llamado NRG-X-Change que utiliza una nueva moneda digital descentralizada para el intercambio de energía, llamada NRGcoin.Postprint (published version

Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction

Insulators installed outdoors are vulnerable to the accumulation of contaminants on their surface, which raise their conductivity and increase leakage current until a flashover occurs. To improve the reliability of the electrical power system, it is possible to evaluate the development of the fault in relation to the increase in leakage current and thus predict a shutdown might occur. This paper proposes the use of empirical wavelet transform (EWT) to reduce the influence of non-representative variations and combines the attention mechanism with long short-term memory (LSTM) recurrent network for prediction. The Optuna framework has been applied for hyperparameter optimization, resulting in a method called Optimized EWT-Seq2Seq-LSTM with Attention. The proposed model had a 10.17% lower mean square error (MSE) than the standard LSTM and a 5.36% lower MSE than the model without optimization, showing that the attention mechanism and hyperparameter optimization is a promising strategy

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models

Artificial intelligence in wind speed forecasting: a review

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values