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

Identification of Influential Climate Indicators, Prediction of Long-Term Streamflow and Great Salt Lake Elevation Using Machine Learning Approach

To meet the surging water demand due to rapid population growth and changing climatic conditions around the world, and to reduce the impact of floods and droughts, comprehensive water management and planning is necessary. Climatic variability, hydrologic uncertainty and variability of hydrologic quantities in time and space are inherent to hydrological modeling. Hydrologic modeling using a physically-based model can be very complex and typically requires detailed knowledge of physical processes. The availability of data is an important issue to justify the use of these models. Data-driven models are an alternative choice. This is a relatively new and efficient approach to modeling. Data-drive models bridge the gap between the classical regression and physically-based models. By using a data-driven model that relies on the machine learning approach, it is possible to produce reasonable predictions from a limited data set and limited knowledge of underlying physical processes of the system by just relating input and output. This dissertation uses the Multivariate Relevance Vector Machine (MVRVM) and Support Vector Machine (SVM) for predicting a variety of hydrological quantities. These models are used in this dissertation for identifying influential climate indicators, and are used for long-term streamflow prediction for multiple lead times at different locations in Utah. They are also used for prediction of Great Salt Lake (GSL) elevation series. They provide reasonable predictions of hydrological quantities from the available data. The predictions from these models are robust and parsimonious. This research presents the first attempt to identify influential climate indicators and predict long lead-time streamflow in Utah, and to predict lake elevation using machine learning models. The approach presented herein has potential value for water resources planning and management especially for irrigation and flood management

Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm

Linear regression is widely used in flood quantile study that consists of meteorological and physiographical variables. However, linear regression does not capture the complex nonlinear relationship between predictor and target variables. It is rare to find a hydrological application using the group method of data handling (GMDH) model, artificial bee colony (ABC) algorithm, and ensemble technique, precisely predicting ungauged sites. GMDH model is known to be an effective model in complying with a nonlinear relationship. Therefore, in this paper, we enhance the GMDH model by implementing the ABC algorithm to optimize the parameter of partial description GMDH model with some transfer functions, namely polynomial, radial basis, sigmoid and hyperbolic tangent function. Then, ensemble averaging combines the output from those various transfer functions and becomes the new ensemble GMDH model coupled with the ABC algorithm (EGMDH-ABC) model. The results show that this method significantly improves the prediction performance of the GMDH model. The EGMDH-ABC model satisfies the nonlinearity in data to produce a better estimation. Also, it provides more robust, accurate, and efficient results

Evaluating and developing parameter optimization and uncertainty analysis methods for a computationally intensive distributed hydrological model

This study focuses on developing and evaluating efficient and effective parameter calibration and uncertainty methods for hydrologic modeling. Five single objective optimization algorithms and six multi-objective optimization algorithms were tested for automatic parameter calibration of the SWAT model. A new multi-objective optimization method (Multi-objective Particle Swarm and Optimization & Genetic Algorithms) that combines the strengths of different optimization algorithms was proposed. Based on the evaluation of the performances of different algorithms on three test cases, the new method consistently performed better than or close to the other algorithms. In order to save efforts of running the computationally intensive SWAT model, support vector machine (SVM) was used as a surrogate to approximate the behavior of SWAT. It was illustrated that combining SVM with Particle Swarm and Optimization can save efforts for parameter calibration of SWAT. Further, SVM was used as a surrogate to implement parameter uncertainty analysis fo SWAT. The results show that SVM helped save more than 50% of runs of the computationally intensive SWAT model The effect of model structure on the uncertainty estimation of streamflow simulation was examined through applying SWAT and Neural Network models. The 95% uncertainty intervals estimated by SWAT only include 20% of the observed data, while Neural Networks include more than 70%. This indicates the model structure is an important source of uncertainty of hydrologic modeling and needs to be evaluated carefully. Further exploitation of the effect of different treatments of the uncertainties of model structures on hydrologic modeling was conducted through applying four types of Bayesian Neural Networks. By considering uncertainty associated with model structure, the Bayesian Neural Networks can provide more reasonable quantification of the uncertainty of streamflow simulation. This study stresses the need for improving understanding and quantifying methods of different uncertainty sources for effective estimation of uncertainty of hydrologic simulation

Applications of machine learning to water resources management: A review of present status and future opportunities

Data availability: No data was used for the research described in the article.The corrected proof will be replaced by version of record in due course.Copyright © 2024 The Authors. Water is the most valuable natural resource on earth that plays a critical role in the socio-economic development of humans worldwide. Water is used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at a global scale, coupled with the predicted climate change-induced impacts, warrants the need for proactive and effective management of water resources. Over the recent decades, machine learning tools have been widely applied to various water resources management-related fields and have often shown promising results. Despite the publication of several review articles on machine learning applications in water-related fields, this review paper presents for the first time a comprehensive review of machine learning techniques applied to water resources management, focusing on the most recent achievements. The study examines the potential for advanced machine learning techniques to improve decision support systems in the various sectors within the realm of water resources management, which includes groundwater management, streamflow forecasting, water distribution systems, water quality and wastewater treatment, water demand and consumption, hydropower and marine energy, water drainage systems, and flood management and defence. This study provides an overview of the state-of-the-art machine learning approaches to the water industry and how they can be used to ensure water supply sustainability, quality, and flood and drought mitigation. This review covers the most recent related studies to provide the most recent snapshot of machine learning applications in the water industry. Overall, LSTM networks have been proven to exhibit reliable performance, often outperforming ANN models, traditional machine learning models, and established physics-based models. Hybrid ML techniques have exhibited great forecasting accuracy across all water-related fields, often showing superior computational power over traditional ANNs architectures. In addition to purely data-driven models, physical-based hybrid models have also been developed to improve prediction performance. These efforts further demonstrate that Machine learning can be a powerful practical tool for water resources management. It provides insights, predictions, and optimisation capabilities to help enhance sustainable water use and management and improve socio-economic development, healthy ecosystems and human existence.EPSRC project reference 2339403 to S. Sayed and A. Ahmed

Amélioration et désagrégation des données GRACE et GRACE-FO pour l’estimation des variations de stock d’eau terrestre et d’eau souterraine à fine échelle

Abstract : Groundwater is an essential natural resource for domestic, industrial and agricultural uses worldwide. Unfortunately, climate change, excess withdrawal, population growth and other human impacts can affect its dynamics and availability. These excessive demands can lead to lower groundwater levels and depletion of aquifers, and potentially to increased water scarcity. Despite the abundance of lakes and rivers in many parts of Canada, the potential depletion of groundwater remains a major concern, particularly in the southern Prairie. Groundwater is traditionally monitored through in-situ piezometric wells, which are scarcely distributed in Canada and many parts of the world. Consequently, its quantities, distribution and availability are not well known, both spatially and temporally. Fortunately, the launch of the twin satellite systems of Gravity Recovery And Climate Experiment (GRACE) in 2002 and its successor, GRACE Follow-On in 2018 (GRACE-FO) opened up new ways to study groundwater changes. These platforms measure the variations of the Earth's gravity field, which in turn can be related to terrestrial water storage (TWS). The main objective of this thesis is to improve the estimation and spatial resolution of TWS and related groundwater storage changes (GWS), using GRACE and GRACE-FO data. This challenge was addressed through four specific objectives, where original approaches were developed in each case. The first objective was to understand and better take into account the uncertainties associated with the hydrological models (the Global Land Data Assimilation System (GLDAS), and the Water Global Assessment Prognosis hydrological model (WGHM)), generally used in the processing of GRACE or GRACE-FO data. The thesis proposes a new approach based on the Gauss-Markov model to estimate the optimal hydrological parameters from GLDAS, considering six different surface schemes. The Förstner estimator and the best quadratic unbiased estimator of the variance components were used with a least-squares method to estimate the optimal hydrological parameters and their errors. The comparison of the optimal TWS derived from GLDAS to the TWS derived from WGHM showed a very significant correlation of r = 0.91. The correlation obtained with GRACE was r = 0.71, which increased to r = 0.81 when the groundwater component was removed from GRACE. Compared to WGHM and GRACE, the optimal TWS calculated from GLDAS had much smaller errors (RMSE = 7 to 8.5 mm) than those obtained when individual surface schemes are considered (RMSE = 10 to 21 mm); demonstrating the performance of the proposed approach. The second specific objective was to understand regional variations in TWS and their uncertainties. The approach was applied over the Canadian landmass. To achieve the goal, the thesis proposes a new modeling of glacial isostatic adjustment uplift (GIA) in Canada. The comparison of the results of the proposed model and three other existing models with data from 149 very high precision GPS stations demonstrated its superiority in the region considered. The regional approach proposed was then used to extract TWS by correcting the effects of the GIA and leakage. The analyzes showed patterns of significant seasonal variations in TWS, with values ranging between -160 mm and 80 mm. Overall TWS showed a positive slope of temporal variations over the Canadian landmass (+ 6.6 mm/year) with GRACE and GRACE-FO combined. The slope reached up to 45 mm/year in the Hudson Bay region. The third objective was to extract GWS component using a comprehensive rigorous approach to reconstruct, refine and map the variations of GWS and its associated uncertainties. The approach used the methods proposed in the two previous objectives. Moreover, a new filtering approach called Gaussian-Han-Fan (GHF) was developed and integrated into the process in order to have a more robust procedure for extracting information from GRACE and GRACE-FO data. The performance and merits of the proposed filter compared to previous filters were analyzed. Then, the groundwater signal was reconstructed by taking into account all the other components, including surface water variations (estimated using satellite altimetry data). The results showed that the average variations of GWS are between -200 mm and +230 mm in the Canadian Prairies. The maximum and minimum GWS trends were found around the Hudson Bay region (approximately 55 mm/year) and southern Prairies (approximately -20 mm/year), respectively. The error on GWS was around 10% (about 19 mm). The estimated GWS changes were validated using the data from 116 in-situ wells. This validation showed a significant level of correlation (r > |0.70|, P |0.90|, P |0,70|, P |0,90|, P < 10-4, RMSE < 30 mm). Enfin, le dernier objectif consistait à améliorer la résolution spatiale des résultats extraits des données GRACE de 1° à 0.25°. Ainsi, une nouvelle approche basée sur l'ajustement des conditions a d’abord été proposée pour estimer les paramètres hydrologiques optimaux et leurs erreurs. Elle est légèrement différente de la méthode proposée dans le premier objectif. Ensuite, les corrections requises pour extraire les anomalies de TWS et ses incertitudes de manière rigoureuse ont été effectuées suivant la méthodologie présentée à l’objectif 3. Par la suite une nouvelle méthode basée sur la combinaison spectrale-spatiale a été développée pour dériver les anomalies de TWS à échelle réduite (0.25°), en combinant de manière optimale les modèles GRACE et les paramètres hydrologiques. Enfin, les anomalies d’eau souterraines ont été dérivées en utilisant les anomalies de TWS estimées. Les validations ont été faites à partir des données de 75 puits en aquifère non confiné en Alberta. Elles démontrent le potentiel de l’approche proposée avec une corrélation très significative de = 0.80 et un RMSE de 11 mm. Ainsi, la recherche proposée dans la thèse a permis de faire des avancées importantes dans l’extraction d’information sur le stockage total d’eau et les eaux souterraines à partir des données des satellites gravimétriques GRACE et GRACE-FO. Elle propose et valide plusieurs nouvelles approches originales en s’appuyant sur des données in-situ. Elle ouvre également plusieurs nouvelles avenues de recherche, qui permettront de faciliter une utilisation plus opérationnelle de ces types de données à l’échelle régionale, voire locale

Empowering Communities, Beyond Energy Scarcity BIWAES 2021 Biennial International Workshop Advances in Energy Studies

This book focuses on the much-needed efforts to design sustainable, well-being-oriented economies, based on appropriate energy use in all sectors of a country´s development. Carbon neutrality, energy efficiency and effectiveness, renewable energies, circular economy strategies, environmental consequences of energy use, engagement and empowerment of local communities in decision making, energy and environmental impacts of consumer behavior, and finally science-based approaches towards sustainable production and consumption are the main focus of the research activities described within this book. An effort to go beyond energy scarcity, to promote energy communities, to explore new technologies, and overall, to understand and address the population-lifestyle-energy nexus towards increased and shared well-being is the final result jointly provided by the book authors. All in all, the aim is for the book to be the starting point of a deeper research and understanding of the importance of a radical improvement in energy use in society, beyond considering energy as just a resource in the world market. Circular economy aspects are also investigated, showing the energy saving potential associated with the appropriate design and recovery of material resources