Forecasting Models for Integration of Large-Scale Renewable Energy Generation to Electric Power Systems

Amid growing concerns about climate change and non-renewable energy sources deple¬tion, vari¬able renewable energy sources (VRESs) are considered as a feasible substitute for conventional environment-polluting fossil fuel-based power plants. Furthermore, the transition towards clean power systems requires additional transmission capacity. Dynamic thermal line rating (DTLR) is being considered as a potential solution to enhance the current transmission line capacity and omit/postpone transmission system expansion planning, while DTLR is highly dependent on weather variations. With increasing the accommodation of VRESs and application of DTLR, fluctuations and variations thereof impose severe and unprecedented challenges on power systems operation. Therefore, short-term forecasting of large-scale VERSs and DTLR play a crucial role in the electric power system op¬eration problems. To this end, this thesis devotes on developing forecasting models for two large-scale VRESs types (i.e., wind and tidal) and DTLR. Deterministic prediction can be employed for a variety of power system operation problems solved by deterministic optimization. Also, the outcomes of deterministic prediction can be employed for conditional probabilistic prediction, which can be used for modeling uncertainty, used in power system operation problems with robust optimization, chance-constrained optimization, etc. By virtue of the importance of deterministic prediction, deterministic prediction models are developed. Prevalently, time-frequency decomposition approaches are adapted to decompose the wind power time series (TS) into several less non-stationary and non-linear components, which can be predicted more precisely. However, in addition to non-stationarity and nonlinearity, wind power TS demonstrates chaotic characteristics, which reduces the predictability of the wind power TS. In this regard, a wind power generation prediction model based on considering the chaosity of the wind power generation TS is addressed. The model consists of a novel TS decomposition approach, named multi-scale singular spectrum analysis (MSSSA), and least squares support vector machines (LSSVMs). Furthermore, deterministic tidal TS prediction model is developed. In the proposed prediction model, a variant of empirical mode decomposition (EMD), which alleviates the issues associated with EMD. To further improve the prediction accuracy, the impact of different components of wind power TS with different frequencies (scales) in the spatiotemporal modeling of the wind farm is assessed. Consequently, a multiscale spatiotemporal wind power prediction is developed, using information theory-based feature selection, wavelet decomposition, and LSSVM. Power system operation problems with robust optimization and interval optimization require prediction intervals (PIs) to model the uncertainty of renewables. The advanced PI models are mainly based on non-differentiable and non-convex cost functions, which make the use of heuristic optimization for tuning a large number of unknown parameters of the prediction models inevitable. However, heuristic optimization suffers from several issues (e.g., being trapped in local optima, irreproducibility, etc.). To this end, a new wind power PI (WPPI) model, based on a bi-level optimization structure, is put forward. In the proposed WPPI, the main unknown parameters of the prediction model are globally tuned based on optimizing a convex and differentiable cost function. In line with solving the non-differentiability and non-convexity of PI formulation, an asymmetrically adaptive quantile regression (AAQR) which benefits from a linear formulation is proposed for tidal uncertainty modeling. In the prevalent QR-based PI models, for a specified reliability level, the probabilities of the quantiles are selected symmetrically with respect the median probability. However, it is found that asymmetrical and adaptive selection of quantiles with respect to median can provide more efficient PIs. To make the formulation of AAQR linear, extreme learning machine (ELM) is adapted as the prediction engine. Prevalently, the parameters of activation functions in ELM are selected randomly; while different sets of random values might result in dissimilar prediction accuracy. To this end, a heuristic optimization is devised to tune the parameters of the activation functions. Also, to enhance the accuracy of probabilistic DTLR, consideration of latent variables in DTLR prediction is assessed. It is observed that convective cooling rate can provide informative features for DTLR prediction. Also, to address the high dimensional feature space in DTLR, a DTR prediction based on deep learning and consideration of latent variables is put forward. Numerical results of this thesis are provided based on realistic data. The simulations confirm the superiority of the proposed models in comparison to traditional benchmark models, as well as the state-of-the-art models

Predictive modelling of global solar radiation with artificial intelligence approaches using MODIS satellites and atmospheric reanalysis data for Australia

Global solar radiation (GSR) prediction is a prerequisite task for agricultural management and agronomic decisions, including photovoltaic (PV) power generation, biofuel exploration and several other bio-physical applications. Since short-term variabilities in the GSR incorporate stochastic and intermittent behaviours (such as periodic fluctuations, jumps and trends) due to the dynamicity of atmospheric variables, GSR predictions, as required for solar energy generation, is a challenging endeavour to satisfactorily predict the solar generated electricity in a PV system. Additionally, the solar radiation data, as required for solar energy monitoring purposes, are not available in all geographic locations due to the absence of meteorological stations and this is especially true for remote and regional solar powered sites. To surmount these challenges, the universally (and freely available) atmospheric gridded datasets (e.g., reanalysis and satellite variables) integrated into solar radiation predictive models to generate reliable GSR predictions can be considered as a viable medium for future solar energy exploration, utilisation and management. Hence, this doctoral thesis aims to design and evaluate novel Artificial Intelligence (AI; Machine Learning and Deep Learning) based predictive models for GSR predictions, using the European Centre for Medium Range Weather Forecasting (ECMWF) Interim-ERA reanalysis and Moderate Resolution Imaging Spectroradiometer (MODIS) Satellite variables enriched with ground-based weather station datasets for the prediction of both long-term (i.e., monthly averaged daily) as well as the short-term (i.e., daily and half-hourly) GSR. The focus of the study region is Queensland, the sunshine state, as well as a number of major solar cities in Australia where solar energy utilisation is actively being promoted by the Australian State and Federal Government agencies. Firstly, the Artificial Neural Networks (ANN), a widely used Machine Learning model is implemented to predict daily GSR at five different cities in Australia using ECMWF Reanalysis fields obtained from the European Centre for Medium Range Weather Forecasting repository. Secondly, the Self-Adaptive Differential Evolutionary Extreme Learning Machine (i.e., SaDE-ELM) is also proposed for monthly averaged daily GSR prediction trained with ECMWF reanalysis and MODIS satellite data from the Moderate Resolution Imaging Spectroradiometer. Thirdly, a three-phase Support Vector Regression (SVR; Machine Learning) model is developed to predict monthly averaged daily GSR prediction where the MODIS data are used to train and evaluate the model and the Particle Swarm Algorithm (PSO) is used as an input selection algorithm. The PSO selected inputs are further transformed into wavelet subseries via non-decimated Discrete Wavelet Transform to unveil the embedded features leading to a hybrid PSO-W-SVR model, seen to outperform the comparative hybrid models. Fourthly, to improve the accuracy of conventional techniques adopted for GSR prediction, Deep Learning (DL) approach based on Deep Belief Network (DBN) and Deep Neural Network (DNN) algorithms are developed to predict the monthly averaged daily GSR prediction using MODIS-based dataset. Finally, the Convolutional Neural Network (CNN) integrated with a Long Short-Term Memory Network (LSTM) model is used to construct a hybrid CLSTM model which is tested to predict the half-hourly GSR values over multiple time-step horizons (i.e., 1-Day, 1-Week, 2-Week, and 1-Month periods). Here, several statistical, Machine Learning and Deep Learning models are adopted to benchmark the proposed DNN and CLSTM models against conventional models (ANN, SaDE-ELM, SVR, DBN). In this doctoral research thesis, a Global Sensitivity Analysis method that attempts to utilise the Gaussian Emulation Machine (GEM-SA) algorithm is employed for a sensitivity analysis of the model predictors. Sensitivity analysis of selected predictors ascertains that the variables: aerosol, cloud, and water vapour parameters used as input parameters for GSR prediction play a significant role and the most important predictors are seen to vary with the geographic location of the tested study site. A suite of alternative models are also developed to evaluate the input datasets classified into El Niño, La Niña and the positive and negative phases of the Indian Ocean Dipole moment. This considers the impact of synoptic-scale climate phenomenon on long-term GSR predictions. A seasonal analysis of models applied at the tested study sites showed that proposed predictive models are an ideal tool over several other comparative models used for GSR prediction. This study also ascertains that an Artificial Intelligence based predictive model integrated with ECMWF reanalysis and MODIS satellite data incorporating physical interactions of the GSR (and its variability) with the other important atmospheric variables can be considered to be an efficient method to predict GSR. In terms of their practical use, the models developed can be used to assist with solar energy modelling and monitoring in solar-rich sites that have diverse climatic conditions, to further support cleaner energy utilization. The outcomes of this doctoral research program are expected to lead to new applications of Artificial Intelligence based predictive tools for GSR prediction, as these tools are able to capture the non-linear relationships between the predictor and the target variable (GSR). The Artificial Intelligence models can therefore assist climate adaptation and energy policymakers to devise new energy management devices not only for Australia but also globally, to enable optimal management of solar energy resources and promote renewable energy to combat current issues of climate change. Additionally, the proposed predictive models may also be applied to other renewable energy areas such as wind, drought, streamflow, flood and electricity demand for prediction

Algorithms for Fault Detection and Diagnosis

Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

Hybrid artificial intelligence algorithms for short-term load and price forecasting in competitive electric markets

The liberalization and deregulation of electric markets forced the various participants to accommodate several challenges, including: a considerable accumulation of new generation capacity from renewable sources (fundamentally wind energy), the unpredictability associated with these new forms of generation and new consumption patterns, contributing to further electricity prices volatility (e.g. the Iberian market). Given the competitive framework in which market participants operate, the existence of efficient computational forecasting techniques is a distinctive factor. Based on these forecasts a suitable bidding strategy and an effective generation systems operation planning is achieved, together with an improved installed transmission capacity exploitation, results in maximized profits, all this contributing to a better energy resources utilization. This dissertation presents a new hybrid method for load and electricity prices forecasting, for one day ahead time horizon. The optimization scheme presented in this method, combines the efforts from different techniques, notably artificial neural networks, several optimization algorithms and wavelet transform. The method’s validation was made using different real case studies. The subsequent comparison (accuracy wise) with published results, in reference journals, validated the proposed hybrid method suitability.O processo de liberalização e desregulação dos mercados de energia elétrica, obrigou os diversos participantes a acomodar uma série de desafios, entre os quais: a acumulação considerável de nova capacidade de geração proveniente de origem renovável (fundamentalmente energia eólica), a imprevisibilidade associada a estas novas formas de geração e novos padrões de consumo. Resultando num aumento da volatilidade associada aos preços de energia elétrica (como é exemplo o mercado ibérico). Dado o quadro competitivo em que os agentes de mercado operam, a existência de técnicas computacionais de previsão eficientes, constituí um fator diferenciador. É com base nestas previsões que se definem estratégias de licitação e se efetua um planeamento da operação eficaz dos sistemas de geração que, em conjunto com um melhor aproveitamento da capacidade de transmissão instalada, permite maximizar os lucros, realizando ao mesmo tempo um melhor aproveitamento dos recursos energéticos. Esta dissertação apresenta um novo método híbrido para a previsão da carga e dos preços da energia elétrica, para um horizonte temporal a 24 horas. O método baseia-se num esquema de otimização que reúne os esforços de diferentes técnicas, nomeadamente redes neuronais artificiais, diversos algoritmos de otimização e da transformada de wavelet. A validação do método foi feita em diferentes casos de estudo reais. A posterior comparação com resultados já publicados em revistas de referência, revelou um excelente desempenho do método hibrido proposto

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Effect of traffic dataset on various machine-learning algorithms when forecasting air quality

© Emerald Publishing Limited. This is the accepted manuscript version of an article which has been published in final form at https://10.1108/JEDT-10-2021-0554Purpose (limit 100 words) Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic datasets on air quality predictions has not been clearly investigated. This research investigates the effects traffic dataset have on the performance of Machine Learning (ML) predictive models in air quality prediction. Design/methodology/approach (limit 100 words) To achieve this, we have set up an experiment with the control dataset having only the Air Quality (AQ) dataset and Meteorological (Met) dataset. While the experimental dataset is made up of the AQ dataset, Met dataset and Traffic dataset. Several ML models (such as Extra Trees Regressor, eXtreme Gradient Boosting Regressor, Random Forest Regressor, K-Neighbors Regressor, and five others) were trained, tested, and compared on these individual combinations of datasets to predict the volume of PM2.5, PM10, NO2, and O3 in the atmosphere at various time of the day. Findings (limit 100 words) The result obtained showed that various ML algorithms react differently to the traffic dataset despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%. Research limitations/implications (limit 100 words) This research is limited in terms of the study area and the result cannot be generalized outside of the UK as many conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research. Therefore, leaving out a few other ML algorithms. Practical implications (limit 100 words) This study reinforces the belief that the traffic dataset has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form traffic dataset in the development of an air quality prediction model. This implies that developers and researchers in air quality prediction need to identify the ML algorithms that behave in their best interest before implementation. Originality/value (limit 100 words) This will enable researchers to focus more on algorithms of benefit when using traffic datasets in air quality prediction.Peer reviewe

Optimal planning and sizing of an autonomous hybrid energy system using multi stage grey wolf optimization

The continuous increase in energy demand and the perpetual dwindling of fossil fuel coupled with its environmental impact have recently attracted research focus in harnessing renewable energy sources (RES) across the globe. Representing the largest RES, solar and wind energy systems are expanding due to the growing evidence of global warming phenomena. However, variability and intermittency are some of the main features that characterize these RES as a result of fluctuation in weather conditions. Hybridization of multiple sources improves the system’s efficiency and reliability of supply due to the varying nature of the RES. Also, the unavailability of solar radiation (SR) and wind speed (WS) measuring equipment in the meteorological stations necessitates the development of prediction algorithms based on Artificial Intelligent (AI) techniques. This thesis presents an autonomous hybrid renewable energy system for a remote community. The hybrid energy system comprises of a photovoltaic module and wind turbine as the main source of energy. Batteries are used as the energy storage devices and diesel generator as a backup energy supply. A new hybrid Wavelet Transform and Adaptive Neuro-Fuzzy Inference System (WT-ANFIS) is developed for the SR prediction, while a hybrid Particle Swarm Optimization (PSO) and ANFIS (PSO-ANFIS) algorithm is developed for the WS prediction. The prediction accuracy of the proposed WT-ANFIS model was validated by comparison with the conventional ANFIS model, Genetic Algorithm (GA) and ANFIS (GA-ANFIS), and PSO-ANFIS models. The proposed PSO-ANFIS for the WS prediction is also compared with ANFIS and GA-ANFIS models. Also, Root Mean Square Error (RMSE), Correlation Coefficient (r) and Coefficient of Determination (R²) are used as statistical indicators to evaluate the performance of the developed prediction models. Additionally, a techno-economic feasibility analysis is carried out using the SR and WS data predicted to assess the viability of the hybrid solar-wind-battery-diesel system for electricity generation in the selected study area. Finally, a new cost-effective Multi Stage – Grey Wolf Optimization (MS-GWO) algorithm is applied to optimally size the different system components. This is aimed at minimizing the net present cost (NPC) while considering reliability and satisfying the load demand. MS-GWO is evaluated by comparison with PSO, GWO and PSO-GWO algorithms. From the results obtained, the statistical evaluators used for model performance assessment of the SR prediction shows that the hybrid WT-ANFIS model’s accuracy outperforms the PSO-ANFIS model by 65% RMSE and 9% R². Also, from the simulation results, the optimal configuration has an NPC of $1.01 million and cost of energy (COE)$0.110/kWh, with an operating cost of $4,723. The system is environmentally friendly with a renewable fraction of 98.3% and greenhouse gas emission reduction of 65%. Finally, a comparison is done between the proposed MS-GWO algorithm with the PSO, GWO and PSO-GWO algorithms. Based on this comparison, the proposed hybrid MS-GWO algorithm outperforms the individual PSO, GWO and PSO-GWO by 3.17%, 2.53% and 2.11% in terms of NPC and reduces the computational time by 53%, 46% and 36% respectively. Therefore, it can be concluded that the proposed MS-GWO technique can be applied for optimal sizing application globally

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate