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A deep learning approach for electricity price forecasting using stacked autoencoders

A deep learning approach for electricity price forecasting u...

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the 11th IET international conference on Advances in Power System Control, Operation and Management (APSCOM 2018)

作者： Hairong Li Guibin Wang Gangqiang Li Huaizhi Wang Chao Wu Zhongxue Fu College of Mechatronics and Control Engineering Shenzhen University Shenzhen College of Information Engineering Shenzhen University Shenzhen

ISBN: (数字)9781839530043

Electricity price is a core indicator for reflecting the operation of the electricity market and evaluating the efficiency of market competition. It is the basis for decision-making in the electricity market. With the continuous development of global power marketization, the role of electricity price in the electricity market has received increasing attention. Accurate short-term electricity price forecasting can provide guidance for bidding strategies of market participants, thereby reducing bidding risks of participants and providing them with a stable return. However, due to the complex nature of electricity prices such as high volatility, high frequency, nonlinearity and non-stationarity, it is difficult to accurately predict electricity prices in advance. To improve prediction accuracy, this paper proposes a market clearing prices (MCPs) prediction method based on wavelet transforms (WT) and stacked autoencoders (SAEs). the deep learning framework comprises two stages is introduced in this work. First, WT is employed to decompose raw MCPs data into different frequency series for better behaviors. Second, the nonlinear features and invariant structures of each frequency are completely extracted by layer-wise pre-training based SAEs. the effectiveness of the proposed prediction method is verified by using actual price data from the Australian electricity market. the experimental results show that the proposed method obtains more accurate prediction, better versatility and practicability compared with several other prediction methods.

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Augmented Cognition: Enhancing Cognition and Behavior in Complex Human Environments: 11th international conference, AC 2017 Held as Part of HCI international 2017 Vancouver, BC, Canada, July 9–14, 2017 Proceedings, Part II 11th

Augmented Cognition: Enhancing Cognition and Behavior in Com...

引用

11th international conference on Augmented Cognition, AC 2017, held as part of the 19th international conference on Human-Computer Interaction, HCI 2017

作者： Schmorrow, Dylan D. Fidopiastis, Cali M. SoarTech OrlandoFL United States Design Interactive Inc. OrlandoFL United States

ISBN: (纸本)9783319586250

this volume constitutes the proceedings of the 11th international conference on Augmented Cognition, AC 2017, held as part of the international conference on Human-Computer Interaction, HCII 2017, which took place in Vancouver, BC, Canada, in July 2017. HCII 2017 received a total of 4340 submissions, of which 1228 papers were accepted for publication after a careful reviewing process. the papers thoroughly cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application *** two volumes set of AC 2017 presents 81 papers which are organized in the following topical sections: electroencephalography and brain activity measurement, eye tracking in augmented cognition, physiological measuring and bio-sensing, machine learning in augmented cognition, cognitive load and performance, adaptive learning systems, brain-computer interfaces, human cognition and behavior in complex tasks and environments.

关键词： artificial intelligence Brain Computer Interface (BCI) cognitive modeling ergonomics Human Computer Interaction (HCI) human engineering human factors intelligent tutoring systems Internet machine learning robotics sensors signal processing social computing user interfaces virtual reality wearable devices

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Predicting learning Effect by Learner's Behavior in MOOCs 18th

Predicting Learning Effect by Learner's Behavior in MOOCs

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18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Tian, Ye Wen, Yimin Yi, Xinhe Yang, Xi Miao, Yuqing Guilin Univ Elect Technol Sch Comp Sci & Informat Secur 1 Jinji Rd Guilin 541004 Peoples R China Guangxi Dist Educ Dept 69 Zhuxi Ave Nanning 5530021 Peoples R China

ISBN: (纸本)9783319689357;9783319689340

With the fast development of MOOCs in recent years, more and more people start to take MOOCs to perfect themselves. However, there exist high dropout rate and low passing rate of examination in many courses. So it is very important to predict learners' learning effect exactly. For learners who predicted good learning effect, teachers can impose intervention to help these learners to stick to the end of courses, while for predicted bad learning effect, teachers can take measures to help these learners to study harder to improve their learning. In this paper, we first analyze learners' learning behavior data to explore the differences among learners with different categories, then a cascade prediction model is proposed to predict whether a learner can earn certificate in a course. Experiments conducted on a real-world dataset illustrated the effectiveness of the proposed model.

关键词： MOOCs learning effect learning behavior Prediction model Education data mining

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11th international conference on Practical Applications of Computational Biology & Bioinformatics 1st ed. 2017

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丛书名： Advances in intelligent Systems and Computing

2017年

作者： Florentino Fdez-Riverola Mohd Saberi Mohamad Miguel Rocha Juan F. De Paz Tiago Pinto

ISBN: (数字)9783319608167

ISBN: (纸本)9783319608150

Biological and biomedical research are increasingly driven by experimental techniques that challenge our ability to analyse, process and extract meaningful knowledge from the underlying data. the impressive capabilities of next-generation sequencing technologies, together with novel and constantly evolving, distinct types of omics data technologies, have created an increasingly complex set of challenges for the growing fields of Bioinformatics and Computational Biology. the analysis of the datasets produced and their integration call for new algorithms and approaches from fields such as databases, Statistics, data Mining, Machine learning, Optimization, Computer Science and Artificial Intelligence. Clearly, Biology is more and more a science of information and requires tools from the computational sciences. In the last few years, we have seen the rise of a new generation of interdisciplinary scientists with a strong background in the biological and computational sciences. In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance in boosting the research efforts in the field and contributing to the education of a new generation of Bioinformatics scientists. the PACBB17 conference was intended to contribute to this effort and promote this fruitful interaction, with a technical program that included 39 papers spanning many different sub-fields in Bioinformatics and Computational Biology. Further, the conference promoted the interaction of scientists from diverse research groups and with a distinct background (computer scientists, mathematicians, biologists).

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Automatic Motion Segmentation via a Cumulative Kernel Representation and Spectral Clustering 18th

Automatic Motion Segmentation via a Cumulative Kernel Repres...

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18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Ona-Rocha, O. R. Sanchez-Manosalvas, O. T. Umaquinga-Criollo, A. C. Rosero-Montalvo, P. D. Suarez-Zambrano, L. E. Rodriguez-Sotelo, J. L. Peluffo-Ordonez, D. H. Univ Tecn Norte Ibarra Ecuador Univ Fuerzas Armadas ESPE Sangolqui Ecuador Corp Univ Autonoma Narino Pasto Colombia Inst Tecnol Super 17 Julio Yachay Ecuador Univ Autonoma Manizales Manizales Colombia Univ Narino Pasto Colombia

ISBN: (纸本)9783319689357;9783319689340

Dynamic or time-varying data analysis is of great interest in emerging and challenging research on automation and machine learning topics. In particular, motion segmentation is a key stage in the design of dynamic data analysis systems. Despite several studies have addressed this issue, there still does not exist a final solution highly compatible with subsequent clustering/classification tasks. In this work, we propose a motion segmentation compatible with kernel spectral clustering (KSC), here termed KSC-MS, which is based on multiple kernel learning and variable ranking approaches. Proposed KSC-MS is able to automatically segment movements within a dynamic framework while providing robustness to noisy environments.

关键词： Kernel spectral clustering Motion segmentation Time-varying data Variable ranking

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Network Traffic Classification Using Tri-training Based on Statistical Flow Characteristics 16

Network Traffic Classification Using Tri-training Based on S...

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16th IEEE international conference on Trust, Security and Privacy in Computing and Communications / the 11th IEEE international conference on Big data Science and engineering / the 14th IEEE international conference on Embedded Software and Systems

作者： Zhao, Shuyuan Zhang, Yongzheng Chang, Peng Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781509049066

Network traffic classification technique is currently a key part of network security systems. In recent years, some network traffic classification algorithms using machine learning based on packet and flow level features have been proposed, yet the results are frequently disappointing. On the one hand, obtaining a large, representative, training data set that is fully labeled to train a classifier is difficult, time-consuming, and expensive. On the other hand, the classification performance is affected by the new protocols and applications which can produce unknown traffic that existing classification systems cannot identify. To achieve effective and inexpensive classification, we propose a framework based on unsupervised methods and the tri-training method. By two independent clusterings, the proposed method can precisely detect unknown applications and extend labeled flows from a few labeled and many unlabeled flows. Meanwhile, tri-training method can effectively exploit unlabeled flows to enhance the proposed method performance. We implement our approach and evaluate it on two real-world Internet traffic traces. the experimental results demonstrate that the proposed method has more excellent performance in terms of Precision and Recall in comparison with the state-of-the-art approaches and can better handle different data sets.

关键词： Training Clustering algorithms Training data Internet Buildings Robustness Labeling

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High-Accuracy Deep Convolution Neural Network for Image Super-Resolution 18th

High-Accuracy Deep Convolution Neural Network for Image Supe...

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18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Tan, Wen'an Guo, Xiao Nanjing Univ Aeronaut & Astronaut Nanjing Jiangsu Peoples R China Shanghai Polytech Univ Shanghai Peoples R China

ISBN: (纸本)9783319689357;9783319689340

Higher performance is the eternal purpose for super-resolution (SR) methods to pursue. Since the deep convolution neural network is introduced into this issue successfully, many SR methods have achieved impressive results. To further improve the accuracy that current SR methods have achieved, we propose a high-accuracy deep convolution network (HDCN). In this article, deeper network structure is deployed for reconstructing images with a fixed upscaling factor and the magnification becomes alternative by cascading HDCN. L-2 loss function is substituted by a more robust one for reducing the blurry prediction. In addition, gradual learning is adopted for accelerating the rate of convergence and compacting the training process. Extensive experiment results prove the effectiveness of these ingenious strategies and demonstrate the higher-accuracy of proposed model among state-of-the-art SR methods.

关键词： Super-resolution Gradual learning Cascade Charbonnier loss function

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Cost-Sensitive Alternating Direction Method of Multipliers for Large-Scale Classification 18th

Cost-Sensitive Alternating Direction Method of Multipliers f...

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18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Wang, Huihui Shi, Yinghuan Chen, Xingguo Gao, Yang Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Sch Software Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783319689357;9783319689340

Large-scale classification is one of the most significant topics in machine learning. However, previous classification methods usually require the assumption that the data has a balanced class distribution. thus, when dealing with imbalanced data, these methods often present performance degradation. In order to seek the better performance in large-scale classification, we propose a novel Cost-Sensitive Alternating Direction Method of Multipliers method (CSADMM) to deal with imbalanced data in this paper. CSADMM derives the problem into a series of subproblems efficiently solved by a dual coordinate descent method in parallel. In particular, CSADMM incorporates different classification costs for large-scale imbalanced classification by cost-sensitive learning. Experimental results on several large-scale imbalanced datasets show that compared with distributed random forest and fuzzy rule based classification system, CSADMM obtains better classification performance, with the training time is significantly reduced. Moreover, compared with single-machine methods, CSADMM also shows promising results.

关键词： Large-scale classification Cost-sensitive learning Alternating direction method of multipliers Imbalanced data

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intelligent approaches for modeling and optimizing HVAC systems' energy use 11

Intelligent approaches for modeling and optimizing HVAC syst...

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ASME 2017 11th international conference on Energy Sustainability, ES 2017, collocated with the ASME 2017 Power conference Joint with ICOPE 2017, the ASME 2017 15th international conference on Fuel Cell Science, engineering and Technology, and the ASME 2017 Nuclear Forum

作者： Tesiero, Raymond C. Nassif, Nabil Gokaraju, Balakrishna Doss, Daniel Adrian University of West Alabama Computer Information Systems and Technology 105 Guy Hunt Building Station #24 LivingstonAL35470 United States North Carolina A and T State University Civil Architectural and Environmental Engineering 1601 E. Market Street GreensboroNC27411 United States

ISBN: (纸本)9780791857595

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. this can be achieved through the use of computational intelligence and optimization. this paper evaluates model-based optimization processes (OP) for HVAC systems utilizing any computer algebra system (CAS), genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. the OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. the development of several self-learning HVAC models and optimizing the process (minimizing energy use) is tested using data collected from an actual HVAC system. Using this optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC's cooling side including the chiller, pump, and fan. the optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE's new ventilation standard 62.1-2013. this research focuses primarily with: on-line, self-tuning, optimization process (OLSTOP);HVAC design principles;and control strategies within a building automation system (BAS) controller. the HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. the program's algorithms analyze multiple variables (humidity, pressure, temperature, CO2, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the fun

关键词： Cooling

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Efficient Adaptive Implementation of the Serial Schedule Generation Scheme Using Preprocessing and Bloom Filters 11th

Efficient Adaptive Implementation of the Serial Schedule Gen...

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11th international conference on learning and intelligent Optimization (LION)

作者： Karapetyan, Daniel Vernitski, Alexei Univ Essex Inst Analyt & Data Sci Colchester Essex England Univ Essex Dept Math Sci Colchester Essex England

ISBN: (纸本)9783319694047;9783319694030

the majority of scheduling metaheuristics use indirect representation of solutions as a way to efficiently explore the search space. thus, a crucial part of such metaheuristics is a "schedule generation scheme" - procedure translating the indirect solution representation into a schedule. Schedule generation scheme is used every time a new candidate solution needs to be evaluated. Being relatively slow, it eats up most of the running time of the metaheuristic and, thus, its speed plays significant role in performance of the metaheuristic. Despite its importance, little attention has been paid in the literature to efficient implementation of schedule generation schemes. We give detailed description of serial schedule generation scheme, including new improvements, and propose a new approach for speeding it up, by using Bloom filters. the results are further strengthened by automated control of parameters. Finally, we employ online algorithm selection to dynamically choose which of the two implementations to use. this hybrid approach significantly outperforms conventional implementation on a wide range of instances.

关键词： Resource-constrained project scheduling problem Serial schedule generation scheme Bloom filters Online algorithm selection

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