Artificial intelligence for decision making in energy demand-side response

This thesis examines the role and application of data-driven Artificial Intelligence (AI) approaches for the energy demand-side response (DR). It follows the point of view of a service provider company/aggregator looking to support its decision-making and operation. Overall, the study identifies data-driven AI methods as an essential tool and a key enabler for DR. The thesis is organised into two parts. It first provides an overview of AI methods utilised for DR applications based on a systematic review of over 160 papers, 40 commercial initiatives, and 21 large-scale projects. The reviewed work is categorised based on the type of AI algorithm(s) employed and the DR application area of the AI methods. The end of the first part of the thesis discusses the advantages and potential limitations of the reviewed AI techniques for different DR tasks and how they compare to traditional approaches. The second part of the thesis centres around designing machine learning algorithms for DR. The undertaken empirical work highlights the importance of data quality for providing fair, robust, and safe AI systems in DR — a high-stakes domain. It furthers the state of the art by providing a structured approach for data preparation and data augmentation in DR to minimise propagating effects in the modelling process. The empirical findings on residential response behaviour show better response behaviour in households with internet access, air-conditioning systems, power-intensive appliances, and lower gas usage. However, some insights raise questions about whether the reported levels of consumers’ engagement in DR schemes translate to actual curtailment behaviour and the individual rationale of customer response to DR signals. The presented approach also proposes a reinforcement learning framework for the decision problem of an aggregator selecting a set of consumers for DR events. This approach can support an aggregator in leveraging small-scale flexibility resources by providing an automated end-to-end framework to select the set of consumers for demand curtailment during Demand-Side Response (DR) signals in a dynamic environment while considering a long-term view of their selection process

The Use of Artificial Intelligence for Decision Making in the Firm

Táto práca sa zaoberá problematikou predikcie vývoja trendu na kapitálových trhoch pomocou neurónových sietí. V práci je diskutované využitie konvolučných a rekurentných neurónových sietí, Elliottovej vlnovej teórie a scalogramov na predikciu vývoja trendu na kapitálových trhoch. Cieľom práce je návrh systému založeného novom prístupe k predikcii vývoja trendu na kapitálových trhoch pomocou Elliottovej vlnovej teórie. Jadro systému bude tvoriť konvolučná neurónová sieť detekujúca zvolené vzory Elliottovej teórie ich rozpoznávaním zo scalogramov získaných spojitou vlnkovou transformáciou častí historických časových rád vybraných kurzov akcií.This thesis is concerned with future trend prediction on capital markets on the basis of neural networks. Usage of convolutional and recurrent neural networks, Elliott wave theory and scalograms for capital market's future trend prediction is discussed. The aim of this thesis is to propose a novel approach to future trend prediction based on Elliott's wave theory. The proposed approach will be based on the principle of classification of chosen patterns from Elliott's theory by the way of convolutional neural network. To this end scalograms of the chosen Elliott patterns will be created through application of continuous wavelet transform on parts of historical time series of price for chosen stocks.

The Use of Artificial Intelligence for Decision Making in the Firm

Diplomová práce se zabývá vytvořením automatického obchodního systému pro trh s cizími měnami s použitím umělé inteligence a prvky technické analýzy. Ve vlastním návrhu řešení je nejprve pomocí fuzzy logiky vybrána vhodná brokerská společnost pro obchodování a testování. Pro výběr měnových párů pro testování strategie je použito shlukování pomocí samo-organizující se mapy. Konkrétní AOS je vytvořen v platformě MetaTrader 4 s využitím programovacího jazyka MQL4 a knihovny FANN pro tvorbu umělých neuronových sítí.Diploma thesis deals with the creation of an automated trading system for foreign exchange market with the usage of artificial intelligence and elements of technical analysis. In the custom solution design a brokerage company for trading and backtesting is selected with the help of fuzzy logic. For selecting currency pairs for backtesting the strategy on is used method of clustering called self-organizing map. The particular ATS is created in MetaTrader4 platform with the usage of a programming language MQL4 and a FANN library for creating artificial neural networks.

The Use of Artificial Intelligence for Decision Making in the Firm

Diplomová práca sa zaoberá návrhom automatického obchodného systému pre obchodovanie na trhu vybraných komodít, skonštruovaného za pomoci technických indikátorov. Súčasťou je taktiež optimalizácia systému s využitím genetických algoritmov za účelom maximalizácie zisku a stability. Na záver je pripravené ekonomické zhodnotenie dosiahnutých výsledkov.The diploma thesis deals with the design of an automatic trading system for trading on the market of selected commodities, constructed with the help of technical indicators. It also includes system optimization using genetic algorithms to maximize profit and stability. Finally, an economic evaluation of the achieved results is prepared.

Bias mitigation with AIF360: A comparative study

The use of artificial intelligence for decision making raises concerns about the societal impact of such systems. Traditionally, the product of a human decision-maker are governed by laws and human values. Decision-making is now being guided - or in some cases, replaced by machine learning classification which may reinforce and introduce bias. Algorithmic bias mitigation is explored as an approach to avoid this, however it does come at a cost: efficiency and accuracy. We conduct an empirical analysis of two off-the-shelf bias mitigation techniques from the AIF360 toolkit on a binary classification task. Our preliminary results indicate that bias mitigation is a feasible approach to ensuring group fairness

Bias mitigation with AIF360: A comparative study

The use of artificial intelligence for decision making raises concerns about the societal impact of such systems. Traditionally, the product of a human decision-maker are governed by laws and human values. Decision-making is now being guided - or in some cases, replaced by machine learning classification which may reinforce and introduce bias. Algorithmic bias mitigation is explored as an approach to avoid this, however it does come at a cost: efficiency and accuracy. We conduct an empirical analysis of two off-the-shelf bias mitigation techniques from the AIF360 toolkit on a binary classification task. Our preliminary results indicate that bias mitigation is a feasible approach to ensuring group fairness

Reverse logistics applied to E-commerce: A Systematic Literature Review on Methods, Applications, and Trends for a Virtual Sustainable Market / Logística reversa aplicada ao comércio eletrônico: uma revisão sistemática da literatura sobre métodos, aplicações e tendências para um mercado virtual sustentável

The digital transformation of society, strengthened by the social isolation resulting from the COVID-19 pandemic, boosted sales and returns of products in e-commerce. In this sense, reverse logistics in e-commerce (RLec) has become essential to meet environmental legislation and consumer expectations, which evaluate exchange policies on new purchases. In this sense, this article presents a systematic review of the literature and content analysis, from 2009 to 2019, to identify methods of decision making and applications in RLec. Thus, 261 publications were selected, of which 92 met the search criteria related to reverse logistics and only 7 applied to e-commerce. In view of this, the main applications involved network design (26%), remanufacturing (21%) and outsourcing (16%), aiming at reducing costs and identifying barriers in reverse operations. Finally, artificial intelligence for decision making was identified as a competitive differential in reducing the complexity and subjectivity of LRec problems

Digital Twin Applied in the Brazilian Energy Sector

This chapter explores the applications of Digital Twin (DT) technology in the Brazilian energy sector and its impact on businesses and society. It highlights how DT applications have contributed to cost reduction, human error mitigation, operational optimization, and technical failure prediction. The chapter also discusses the implementation process and the requirements for developing these systems. Additionally, it explores the potential of leveraging Artificial Intelligence for decision-making support, utilizing Big Data processes to enhance various areas, and employing User Experience (UX) techniques to streamline outdated processes, through the examination of real projects in the wind power monitoring, transmission towers, and data-saving equipment domains, addressing the challenges faced and the benefits derived from its implementation