2,734 research outputs found
Big Data Analysis application in the renewable energy market: wind power
Entre as enerxías renovables, a enerxía eólica e unha das tecnoloxías
mundiais de rápido crecemento. Non obstante, esta incerteza debería minimizarse para programar e xestionar
mellor os activos de xeración tradicionais para compensar a falta de electricidade nas redes electricas. A aparición
de técnicas baseadas en datos ou aprendizaxe automática deu a capacidade de proporcionar predicións espaciais
e temporais de alta resolución da velocidade e potencia do vento. Neste traballo desenvólvense tres modelos
diferentes de ANN, abordando tres grandes problemas na predición de series de datos con esta técnica: garantía
de calidade de datos e imputación de datos non válidos, asignación de hiperparámetros e selección de funcións.
Os modelos desenvolvidos baséanse en técnicas de agrupación, optimización e procesamento de sinais para
proporcionar predicións de velocidade e potencia do vento a curto e medio prazo (de minutos a horas)
Efficient image retrieval by fuzzy rules from boosting and metaheuristic
Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter. © 2020 Marcin Korytkowski et al., published by Sciendo.program of the Polish Minister of Science and Higher Education under the name "Regional Initiative of Excellence" in the years 2019-2022 [020/RID/2018/19
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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
AI Applications to Power Systems
Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered
A Review of Harmful Algal Bloom Prediction Models for Lakes and Reservoirs
Anthropogenic activity has led to eutrophication in water bodies across the world. This eutrophication promotes blooms, cyanobacteria being among the most notorious bloom organisms. Cyanobacterial blooms (more commonly referred to as harmful algal blooms (HABs)) can devastate an ecosystem. Cyanobacteria are resilient microorganisms that have adapted to survive under a variety of conditions, often outcompeting other phytoplankton. Some species of cyanobacteria produce toxins that ward off predators. These toxins can negatively affect the health of the aquatic life, but also can impact animals and humans that drink or come in contact with these noxious waters. Although cyanotoxin’s effects on humans are not as well researched as the growth, behavior, and ecological niche of cyanobacteria, their health impacts are of large concern. It is important that research to mitigate and understand cyanobacterial blooms and cyanotoxin production continues. This project supports continued research by addressing an approach to collect and summarize published articles that focus on techniques and models to predict cyanobacterial blooms with the goal of understanding what research has been done to promote future work. The following report summarizes 34 articles from 2003 to 2020 that each describe a mechanistic or data driven model developed to predict the occurrence of cyanobacterial blooms or the presence of cyanotoxins in lakes or reservoirs with similar climates to Utah. These articles showed a shift from more mechanistic approaches to more data driven approaches with time. This resulted in a more individualistic approach to modeling, meaning that models are often produced for a single lake or reservoir and are not easily comparable to other models for different systems
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
Application of PSO for optimization of power systems under uncertainty
The primary objective of this dissertation is to develop a black box optimization
tool. The algorithm should be able to solve complex nonlinear, multimodal, discontinuous
and mixed-integer power system optimization problems without any
model reduction. Although there are many computational intelligence (CI) based
algorithms which can handle these problems, they require intense human intervention
in the form of parameter tuning, selection of a suitable algorithm for a given
problem etc. The idea here is to develop an algorithm that works relatively well on
a variety of problems with minimum human effort. An adaptive particle swarm
optimization algorithm (PSO) is presented in this thesis. The algorithm has special
features like adaptive swarm size, parameter free update strategies, progressive
neighbourhood topologies, self learning parameter free penalty approach etc.
The most significant optimization task in the power system operation is the
scheduling of various generation resources (Unit Commitment, UC). The current
practice used in UC modelling is the binary approach. This modelling results in a
high dimension problem. This in turn leads to increased computational effort and
decreased efficiency of the algorithm. A duty cycle based modelling proposed in
this thesis results in 80 percent reduction in the problem dimension. The stern uptime
and downtime requirements are also included in the modelling. Therefore,
the search process mostly starts in a feasible solution space. From the investigations
on a benchmark problem, it was found that the new modelling results in high
quality solutions along with improved convergence.
The final focus of this thesis is to investigate the impact of unpredictable nature
of demand and renewable generation on the power system operation. These quantities
should be treated as a stochastic processes evolving over time. A new PSO
based uncertainty modelling technique is used to abolish the restrictions imposed
by the conventional modelling algorithms. The stochastic models are able to incorporate
the information regarding the uncertainties and generate day ahead UC
schedule that are optimal to not just the forecasted scenario for the demand and
renewable generation in feed but also to all possible set of scenarios. These models
will assist the operator to plan the operation of the power system considering
the stochastic nature of the uncertainties. The power system can therefore optimally
handle huge penetration of renewable generation to provide economic operation
maintaining the same reliability as it was before the introduction of uncertainty
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