Nonlinear Structure based Artificial Neural Computing for Upstream Flow Functional Models

Most of the real world systems are nonlinear and complex and it is challenging to model these types of systems for analyzing and forecasting the hidden behaviour of the systems. In the paradigm of vague complex systems, data-based time series modeling approaches of intelligent systems showed its applicability for coping with the problems of hidden noise and dynamicity which are encapsulated in the data. Getting from nature is one of the humans’ features and they are striving to produce the intellectual schemes by coping rare features of cognitions and intellect of the brain. In this paper, nonlinear autoregressive structure based modeling of the brain (i.e. Artificial Neural Network) is the aim of this study that suggest various Dynamic Neural Network (DNN) models by using time deferred autoregressive configurations, for the stream-flow of Sukkur barrage on lower Indus river basin. The suitability of the models for training, validation and testing stages, are evinced on assessment metrics which demonstrate the accuracy and sufficiency of the models which may be beneficial for water-resource management

Modeling Energy Demand—A Systematic Literature Review

In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.BMBF, 03SFK4T0, Verbundvorhaben ENavi: Energiewende-Navigationssystem zur Erfassung, Analyse und Simulation der systemischen Vernetzungen" - Teilvorhaben T0BMWi, 03ET4040C, Verbundvorhaben: Harmonisierung und Entwicklung von Verfahren zur regional und zeitlich aufgelösten Modellierung von Energienachfragen (DemandRegio) Teilvorhaben: ProfileDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques

Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint

Control System for Electrical Power Grids with Renewables using Artificial Intelligence Methods

Modern electrical and electronic devices are very sensitive to the power supply and require steady and stable electric power. Factories may also need electric power within a specific standard range of voltage, frequency, and current to avoid defects in the production. For these reasons electric power utilities must produce an electric power of a specific standard of power quality parameters [EN50160]. Nowadays, renewable energy sources, such as wind energy and solar energy are used to generate electric power as free and clean power sources as well to reduce fuel consumption and environmental pollution as much as possible. Renewable energy, e.g. wind speed or solar irradiance, are not stable or not constant energies over the time. Therefore smart control systems (SCSs) are needed for operate the power system in optimal way which help for producing a power with good quality from renewable sources. The forecasting and prediction models play a main role in these issues and contribute as the important part of the smart control system (SCS). The main task of the SCS is to keep the generated power equal to the consumed power as well as to consider standard levels of power quality parameters as much as possible. Some of previous studies have focused on forecasting power quality parameters, power load, wind speed and solar irradiance using machine learning models as neural networks, support vector machines, fuzzy sets, and neuro fuzzy. This thesis proposes designing forecasting systems using machine learning techniques in order to be use in control and operate an electrical power system. In this study, design and tested forecasting systems related to the power and renewable energies. These systems include wind speed forecasting, power load forecasting and power quality parameters forecasting. The main part of this thesis is focus in power quality parameters forecasting in short-term, these parameters are: power frequency, magnitude of the supply voltage, total harmonic distortion of voltage (THDu), total harmonic distortion of current (THDi), and short term flicker severity (Pst) according to the definition in [EN50160]. The output of the forecasting models of power quality parameters can be used in shifting the load to run in switch time which will help for correct and optimize the quality of the power.Modern electrical and electronic devices are very sensitive to the power supply and require steady and stable electric power. Factories may also need electric power within a specific standard range of voltage, frequency, and current to avoid defects in the production. For these reasons electric power utilities must produce an electric power of a specific standard of power quality parameters [EN50160]. Nowadays, renewable energy sources, such as wind energy and solar energy are used to generate electric power as free and clean power sources as well to reduce fuel consumption and environmental pollution as much as possible. Renewable energy, e.g. wind speed or solar irradiance, are not stable or not constant energies over the time. Therefore smart control systems (SCSs) are needed for operate the power system in optimal way which help for producing a power with good quality from renewable sources. The forecasting and prediction models play a main role in these issues and contribute as the important part of the smart control system (SCS). The main task of the SCS is to keep the generated power equal to the consumed power as well as to consider standard levels of power quality parameters as much as possible. Some of previous studies have focused on forecasting power quality parameters, power load, wind speed and solar irradiance using machine learning models as neural networks, support vector machines, fuzzy sets, and neuro fuzzy. This thesis proposes designing forecasting systems using machine learning techniques in order to be use in control and operate an electrical power system. In this study, design and tested forecasting systems related to the power and renewable energies. These systems include wind speed forecasting, power load forecasting and power quality parameters forecasting. The main part of this thesis is focus in power quality parameters forecasting in short-term, these parameters are: power frequency, magnitude of the supply voltage, total harmonic distortion of voltage (THDu), total harmonic distortion of current (THDi), and short term flicker severity (Pst) according to the definition in [EN50160]. The output of the forecasting models of power quality parameters can be used in shifting the load to run in switch time which will help for correct and optimize the quality of the power.410 - Katedra elektroenergetikyvyhově

メタヒューリスティクスおよび機械学習を用いた建物・地域エネルギーシステムの最適化に関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 大岡 龍三, 東京大学教授 加藤 信介, 東京大学教授 赤司 泰義, 東京大学教授 合原 一幸, 東京大学講師 菊本 英紀University of Tokyo(東京大学

The Analysis and Application of Artificial Neural Networks for Early Warning Systems in Hydrology and the Environment

Final PhD thesis submissionArtificial Neural Networks (ANNs) have been comprehensively researched, both from a computer scientific perspective and with regard to their use for predictive modelling in a wide variety of applications including hydrology and the environment. Yet their adoption for live, real-time systems remains on the whole sporadic and experimental. A plausible hypothesis is that this may be at least in part due to their treatment heretofore as “black boxes” that implicitly contain something that is unknown, or even unknowable. It is understandable that many of those responsible for delivering Early Warning Systems (EWS) might not wish to take the risk of implementing solutions perceived as containing unknown elements, despite the computational advantages that ANNs offer. This thesis therefore builds on existing efforts to open the box and develop tools and techniques that visualise, analyse and use ANN weights and biases especially from the viewpoint of neural pathways from inputs to outputs of feedforward networks. In so doing, it aims to demonstrate novel approaches to self-improving predictive model construction for both regression and classification problems. This includes Neural Pathway Strength Feature Selection (NPSFS), which uses ensembles of ANNs trained on differing subsets of data and analysis of the learnt weights to infer degrees of relevance of the input features and so build simplified models with reduced input feature sets. Case studies are carried out for prediction of flooding at multiple nodes in urban drainage networks located in three urban catchments in the UK, which demonstrate rapid, accurate prediction of flooding both for regression and classification. Predictive skill is shown to reduce beyond the time of concentration of each sewer node, when actual rainfall is used as input to the models. Further case studies model and predict statutory bacteria count exceedances for bathing water quality compliance at 5 beaches in Southwest England. An illustrative case study using a forest fires dataset from the UCI machine learning repository is also included. Results from these model ensembles generally exhibit improved performance, when compared with single ANN models. Also ensembles with reduced input feature sets, using NPSFS, demonstrate as good or improved performance when compared with the full feature set models. Conclusions are drawn about a new set of tools and techniques, including NPSFS and visualisation techniques for inspection of ANN weights, the adoption of which it is hoped may lead to improved confidence in the use of ANN for live real-time EWS applications.EPSRCUKWIRThe Environment Agenc