Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The authors discuss high-voltage power conversion. Conventional series connection and three-level voltage source inverter techniques are reviewed and compared. A novel versatile multilevel commutation cell is introduced: it is shown that this topology is safer and more simple to control, and delivers purer output waveforms. The authors show how this technique can be applied to either choppers or voltage-source inverters and generalized to any number of switches.<<ETX>>

Multi-level conversion : high voltage choppers and voltage-source inverters

https://repository.kaust.edu.sa/bitstream/10754/562573/1/Economics%20of%20water%20desalination%20paper%20Desalination.pdf

Technical review and evaluation of the economics of water desalination: Current and future challenges for better water supply sustainability

Annual Energy Outlook 2008 With Projections to 2030

The energy delivery of a solar-energy system is generally associated with the sun's available irradiance and spectral content, as well as a variety of environmental and climatic factors and inherent system and component performances. However, other external factors relating to geographical location and conditions can have even greater impacts on system performance. Among these, soiling is a commonly overlooked or underestimated issue that can be a showstopper for the viability of a solar installation. This paper provides a comprehensive overview of soiling problems, primarily those associated with “dust” (sand) and combined dust–moisture conditions that are inherent to many of the most solar-rich geographic locations worldwide. We review and evaluate key contributions to the understanding, performance effects, and mitigation of these problems. These contributions span a technical history of almost seven decades. We also present an inclusive literature survey/assessment. The focus is on both transmissive surfaces (e.g., those used for flat-plate photovoltaics or for concentrating lenses) and reflective surfaces (e.g., mirrors or heliostats for concentrating power systems).

A comprehensive review of the impact of dust on the use of solar energy: History, investigations, results, literature, and mitigation approaches

Review of optimization techniques applied for the integration of distributed generation from renewable energy sources

PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation

This paper focuses on a Matlab/SIMULINK model of a photovoltaic cell. This model is based on mathematical equations and is described through an equivalent circuit including a photocurrent source, a diode, a series resistor and a shunt resistor. The developed model allows the prediction of PV cell behaviour under different physical and environmental parameters. The model can also be used to extract the physical parameters for a given solar PV cell as a function of temperature and solar radiation. In addition, this study outlines the working principle of PV module as well as PV array. In order to validate the developed model, an experimental test bench was built and the obtained results exhibited a good agreement with the simulation ones. Keywords-Matlab, SIMULINK, PV, solar cell model, solar array model, solar radiation, maximum power point

https://ijrer.com/index.php/ijrer/article/viewFile/157/pdf

MATLAB/Simulink Based Modelling of Solar Photovoltaic Cell

This article provides policy-makers and renewable energy project developers with background information and analysis into the successful penetration of renewable energy policies. The analysis emphasizes on the different mechanisms to establish an encouraging regulatory framework for renewable energies and examines examples of both successful and failed experiences, through case studies and analysis of various countries. This analytic survey attempts to shed light on the factors which led to successful implementation of renewable energy depending not only on different countries experience, but also on the different sources and technologies for renewable electricity. The main objectives through the provision of this overview are to help policy implementers learn from each other׳s experiences and contribute to the efforts to meet indicative renewable energy targets. The methodology applied in this document is to collect all applied mechanisms helping deploying renewable energy projects with a reviewing of study cases analysis for some specific experiences. Then the information are classified and discussed from the financial, fiscal, legislative, political, technological and environmental points of view in order to make it a reference and a guideline for other renewable energy policies studies.

Review of policies encouraging renewable energy integration & best practices

Wind energy resource assessment applications require accurate wind measurements. Most of the published studies used data from existing weather station network operated by meteorological departments. Due to relatively high cost of weather stations the resolution of the weather station network is coarse for wind energy applications. Typically, meteorological departments install weather stations at specific locations such as airports, ports and areas with high density population. Typically, these locations are avoided during wind farms siting. According to WMO regulations, weather stations provide measurements for different weather elements at specific altitudes such as 2 m for air temperature and 10 m for wind measurements. For wind energy resource assessment applications, minimum of one year of wind measurements is required to build wind climatology for a certain site. Therefore data collected from a certain site cannot be used before one year of operation. Due to these limitations, wind energy resource assessment application needs to use data from different sources. Recently, wind assessment studies were conducted using data generated by Numerical Weather Prediction models. This paper reviews the use of the Numerical Weather Prediction data for wind energy resource assessment. It gives a general overview of NWP models and how they overcome the limitations in the classical wind measurements.

Adel Gastli

Papers

Review of optimization techniques applied for the integration of distributed generation from renewable energy sources

PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation

MATLAB/Simulink Based Modelling of Solar Photovoltaic Cell

Review of policies encouraging renewable energy integration & best practices

Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment