Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods

Improvement of environmental aspects of thermal power plant operation by advanced control concepts

The necessity of the reduction of greenhouse gas emissions, as formulated in the Kyoto Protocol, imposes the need for improving environmental aspects of existing thermal power plants operation. Improvements can be reached either by efficiency increment or by implementation of emission reduction measures. Investments in refurbishment of existing plant components or in plant upgrading by flue gas desulphurization, by primary and secondary measures of nitrogen oxides reduction, or by biomass co-firing, are usually accompanied by modernisation of thermal power plant instrumentation and control system including sensors, equipment diagnostics and advanced controls. Impact of advanced control solutions implementation depends on technical characteristics and status of existing instrumentation and control systems as well as on design characteristics and actual conditions of installed plant components. Evaluation of adequacy of implementation of advanced control concepts is especially important in Western Balkan region where thermal power plants portfolio is rather diversified in terms of size, type and commissioning year and where generally poor maintenance and lack of investments in power generation sector resulted in high greenhouse gases emissions and low efficiency of plants in operation. This paper is intended to present possibilities of implementation of advanced control concepts, and particularly those based on artificial intelligence, in selected thermal power plants in order to increase plant efficiency and to lower pollutants emissions and to comply with environmental quality standards prescribed in large combustion plant directive. [Acknowledgements. This paper has been created within WBalkICT - Supporting Common RTD actions in WBCs for developing Low Cost and Low Risk ICT based solutions for TPPs Energy Efficiency increasing, SEE-ERA.NET plus project in cooperation among partners from IPA SA - Romania, University of Zagreb - Croatia and Vinca Institute from Serbia and. The project has initiated a strong scientific cooperation, with innovative approaches, high scientific level, in order to correlate in an optimal form, using ICT last generation solutions, the procedures and techniques from fossil fuels burning processes thermodynamics, mathematical modelling, modern methods of flue gases analysis, combustion control, Artificial Intelligence Systems with focus on Expert Systems category.

Hybrid Automaton Based Vehicle Platoon Modelling and Cooperation Behaviour Profile Prediction

Autonomous cooperative driving systems require the integration of research activities in the field of embedded systems, robotics, communication, control and artificial intelligence in order to create a secure and intelligent autonomous drivers behaviour patterns in the traffic. Beside autonomous vehicle management, an important research focus is on the cooperation behaviour management. In this paper, we propose hybrid automaton modelling to emulate flexible vehicle Platoon and vehicles cooperation interactions. We introduce novel coding function for Platoon cooperation behaviour profile generation in time, which depends of vehicles number in Platoon and behaviour types. As the behaviour prediction of transportation systems, one of the primarily used methods of artificial intelligence in Intelligent Transport Systems, we propose an approach towards NARX neural network prediction of Platoon cooperation behaviour profile. With incorporation of Platoon manoeuvres dynamic prediction, which is capable of analysing traffic behaviour, this approach would be useful for secure implementation of real autonomous vehicles cooperation

Analysis of dynamic conflicts by techniques of artificial intelligence

Dynamic conflicts exhibit differentiel game characteristics and their analysis by any method which disregards this feature may be, by definition, futile. Unfortunately, realistic conflicts may have an intricate information structure and a complex hierarchy which don't fit in the classical differential game formulation. Moreover, in many cases even well formulated differential games are not solvable. In the recent years great progress has been made in artificial intelligence techniques, put in evidence by successfull applications in scientifique modelling, automated engineering design processes as well as for fuzzy and intelligent control systems. This progress has raised hopes that artificial intelligence methods can be of help also in solving complex dynamic conflicts. This scientific report outlines a feasible option which combines artificial intelligence techniques with concepts of differential game theory for attaining such an objective. A research effort in this direction has a great potential of success, but it requires a well planned and coordinated collaboration of qualified scientist in both disciplines

Putting a Face on Algorithms: Personas for Modeling Artificial Intelligence

We propose a new type of personas, artificial intelligence (AI) personas, as a tool for designing systems consisting of both human and AI agents. Personas are commonly used in design practices for modelling users. We argue that the personification of AI agents can help multidisciplinary teams in understanding and designing systems that include AI agents. We propose a process for creating AI personas and the properties they should include, and report on our first experience using them. The case we selected for our exploration of AI personas was the design of a highly automated decision support tool for air traffic control. Our first results indicate that AI personas helped designers to empathise with algorithms and enabled better communication within a team of designers and AI and domain experts. We call for a research agenda on AI personas and discussions on potential benefits and pitfalls of this approach.acceptedVersio

A survey of agent-oriented methodologies

This article introduces the current agent-oriented methodologies. It discusses what approaches have been followed (mainly extending existing object oriented and knowledge engineering methodologies), the suitability of these approaches for agent modelling, and some conclusions drawn from the survey