Machine learning and deep learning

Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.Comment: Published online first in Electronic Market

Towards autonomous decision-making: A probabilistic model for learning multi-user preferences

Information systems have revolutionized the provisioning of decision-relevant information, and decision support tools have improved human decisions in many domains. Autonomous decision- making, on the other hand, remains hampered by systems’ inability to faithfully capture human preferences. We present a computational preference model that learns unobtrusively from lim- ited data by pooling observations across like-minded users. Our model quantifies the certainty of its own predictions as input to autonomous decision-making tasks, and it infers probabilistic segments based on user choices in the process. We evaluate our model on real-world preference data collected on a commercial crowdsourcing platform, and we find that it outperforms both individual and population-level estimates in terms of predictive accuracy and the informative- ness of its certainty estimates. Our work takes an important step toward systems that act autonomously on their users’ behalf

Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex socio-technical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating them requires research methods that scale beyond the traditional areas of inquiry of Information Systems (IS) “individuals, organizations, and markets” and that deliver solutions in addition to insights. We describe an approach to address these challenges through Competitive Benchmarking (CB), a novel research method that helps interdisciplinary research communities to tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation. Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions

Business intelligence in the electrical power industry

Nowadays, the electrical power industry has gained tremendous interest from both entrepreneurs and researchers due to its essential roles in everyday life. However, the current sources for generating electricity are astonishing decreasing, which leads to more challenges for the power industry. Based on the viewpoint of sustainable development, the solution should maintain three layers of economically, ecologically, and society; simultaneously, support business decision-making, increases organizational productivity and operational energy efficiency. In the smart and innovative technology context, business intelligence solution is considered as a potential option in the data-rich environment, which is still witnessed disjointed theoretical progress. Therefore, this study aimed to conduct a systematic literature review and build a body of knowledge related to business intelligence in the electrical power sector. The author also built an integrative framework displaying linkages between antecedents and outcomes of business intelligence in the electrical power industry. Finally, the paper depicted the underexplored areas of the literature and shed light on the research objectives in terms of theoretical and practical implications

New actor types in electricity market simulation models: Deliverable D4.4

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: The modelling of agents in the simulation models and tools is of primary importance if the quality and the validity of the simulation outcomes are at stake. This is the first version of the report that deals with the representation of electricity market actors’ in the agent based models (ABMs) used in TradeRES project. With the AMIRIS, the EMLab-Generation (EMLab), the MASCEM and the RESTrade models being in the centre of the analysis, the subject matter of this report has been the identification of the actors’ characteristics that are already covered by the initial (with respect to the project) version of the models and the presentation of the foreseen modelling enhancements. For serving these goals, agent attributes and representation methods, as found in the literature of agent-driven models, are considered initially. The detailed review of such aspects offers the necessary background and supports the formation of a context that facilitates the mapping of actors’ characteristics to agent modelling approaches. Emphasis is given in several approaches and technics found in the literature for the development of a broader environment, on which part of the later analysis is deployed. Although the ABMs that are used in the project constitute an important part of the literature, they have not been included in the review since they are the subject of another section.N/

Machine Learning Algorithms for Smart Electricity Markets

__Abstract__ The shift towards sustainable electricity systems is one of the grand challenges of the twenty-first century. Decentralized production from renewable sources, electric mobility, and related advances are at odds with traditional power systems where central large-scale generation of electricity follows inelastic consumer demand. Smart Markets and intelligent Information Systems (IS) could alleviate these issues by providing new forms of coordination that leverage real-time consumption information and prices to incentivize behaviors that remain within the grid's operational bounds. However, the best design for these artifacts, and the societal implications of different design choices is largely unclear. This dissertation makes three contributions to the debate. First, we propose and study a design for Brokers, a novel type of IS for autonomous intermediation in retail electricity markets. Second, we propose a probabilistic model for representing customer preferences within intelligent IS, and we study its performance in electricity tariff and other choice tasks. And third, we propose and study Competitive Benchmarking, a novel research method for effective IS artifact design in complex environments like Smart Grids where the social cost of failure is prohibitive. Our results provide guidance on IS design choices for sustainable electricity systems, and they highlight their potential societal positives and negatives

D4.4 - New actor types in electricity market simulation models

The modelling of agents in the simulation models and tools is of primary importance if the quality and the validity of the simulation outcomes are at stake. This is the final version of the report that deals with the representation of electricity market actors’ in the agent-based models (ABMs) used in TradeRES project and it was developed within the scope of Task 4.2 - Representation of new actors, markets and policies. With the ABMs available in the consortium (AMIRIS, the EMLab, the MASCEM and the RESTrade) being in the centre of the analysis, the subject matter of this report has been the identification of the actors’ characteristics that are already covered by the initial (with respect to the project) version of the models and the presentation of the foreseen modelling enhancements

A reinforcement learning approach to autonomous decision-making in Smart Electricity Markets

For the vision of a Smart Grid to materialize, substantial advances in intelligent decentralized control mechanisms are required. We propose a novel class of autonomous broker agents for retail electricity trading that can operate in a wide range of Smart Electricity Markets, and that are capable of deriving long-term, profit-maximizing policies. Our brokers use Reinforcement Learning with function approximation, they can accommodate arbitrary economic signals from their environments, and they learn efficiently over the large state spaces resulting from these signals. Our design is the first that can accommodate an offline training phase so as to automatically optimize the broker for particular market conditions. We demonstrate the performance of our design in a series of experiments using real-world energy market data, and find that it outperforms previous approaches by a significant margin

Bayesian Filtering Methods For Dynamic System Monitoring and Control

Real-time system monitoring and control represent two of the most important issues that characterize modern industries in critical areas of civilian and military interest, including the power grid, energy, healthcare, aerospace, and infrastructure. During the past decade, there has been a rapid development of robust dynamic system monitoring and control methods for fault diagnosis and failure prognosis. Among various monitoring and control policies, condition-based maintenance (CBM) has been studied by many researchers due to its ability to enable a large amount of monitoring data for real-time diagnostics and prognostics. A considerable amount of literature has been published on the subject, providing a large volume of dynamic system control methods. Previously published studies are limited by assumptions that can generally be distinguished into three main categories: i) predefined system failure thresholds, ii) simplified latent dynamics, and iii) unrealistic parametric forms that describe the evolution of system dynamics through time. This thesis provides an array of solution approaches that overcome the aforementioned assumptions in a smart and effective way by introducing novel quantitative frameworks for real-time monitoring, control, and decision-making for dynamic systems. The proposed frameworks are categorized into two main phases of a comprehensive framework. The first phase contains two original Bayesian filtering methods for condition monitoring and control of systems with either linear or non-linear degradation dynamics. The former is designed only for systems with linear latent and observable dynamics and utilizes Kalman filtering for state-parameter inference. It considers a failure process that is purely stochastic and is based on logistic regression. This process is directly affected by the latent system dynamics, therefore avoiding the need for a priori failure thresholds. The latter takes into consideration multiple levels of system dynamics that evolve either linearly or non-linearly. A hybrid particle filter is developed for state-parameter inference, while an Extreme Learning Machine artificial neural network is utilized to relate sensor observations to latent system dynamics. Both frameworks are tested and validated on synthetic and real-world time-series datasets. The second phase of this thesis introduces an original method for optimal control and decision-making that employs Bayesian filtering-based deep reinforcement learning with fully stochastic environments. Sets of deep reinforcement learning agents were trained to develop control policies. Bayesian filtering methods from the first phase were utilized to provide environment states that use the estimates from latent system dynamics. This method is used in two different applications for maintenance cost minimization and estimating the remaining useful life of a system under condition monitoring. Results obtained from applying the framework on simulated and real-world time-series data suggest that the proposed Bayesian filtering-based deep reinforcement learning algorithm can be trained even with limited data, which can be useful for real-time control and decision making for many dynamic systems

An e-business model for the participation of households in the Serbian electricity market based on smart grid technologies

Предмет истраживања дисертације је развој модела електронског пословања заснованог на флексибилном учешћу потрошача на српском тржишту електричне енергије. Циљ је развoj одрживoг и применљивoг моделa електронског пословања који омогућава учешће домаћинстава и појединачних уређаја на балансном тржишту и на берзи електричне енергије коришћењем smart grid технологија и demand-response сервиса. Учествовањем појединачних домаћинстава и уређаја у балансном тржишту омогућава се прецизна контрола фреквенције електроенергетског система, што oтвара могућности за експлоатацију обновљивих извора електричне енергије преко флексибилнијег и тачнијег управљања флуктуацијама. Tржиштa електричне енергије традиционално функционишу по B2B моделу електронског пословања где се пословање врши искључиво између произвођача и оператора тржишта. Дерегулација отвара нове могућности пословања за тржишта електричне енергије, претежно за домаћинства чијом се инклузијом у великој мери може повећати флексибилност и ефикасност механизма балансирања и целокупна стабилност електроенергетске мреже. Пословни модел предложен у овој дисертацији прилагођен је пословном окружењу и регулативи у Републици Србији. Предложена техничка спецификација модела састоји се од дистрибуиране инфраструктуре за повезивање и координацију великог броја корисничких IoT уређаја. За прикупљање, анализу и управљање подацима у реалном времену користе се аналитичке методе и софтверски алати базирани на пословној интелигенцији. Предност предложеног модела је у могућности примене на тржиштима електричне енергије која су у развоју, ниским иницијалним инвестицијама и интеграцији са smart grid технологијама. У дисертацији су приказани истраживање и анализа спремности потрошача и учесника на тржишту, за примену предложеног модела