Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: a mini review

Abstract:Site suitability problems in renewable energy studies have taken a new turn since the advent of geographical information system (GIS). GIS has been used for site suitability analysis for renewable energy due to its prowess in processing and analyzing attributes with geospatial components. Multi-criteria decision making (MCDM) tools are further used for criteria ranking in the order of influence on the study. Upon location of most appropriate sites, the need for intelligent resource forecast to aid in strategic and operational planning becomes necessary if viability of the investment will be enhanced and resource variability will be better understood. One of such intelligent models is the adaptive neuro-fuzzy inference system (ANFIS) and its variants. This study presents a mini-review of GIS-based MCDM facility location problems in wind and solar resource site suitability analysis and resource forecast using ANFIS-based models. We further present a framework for the integration of the two concepts in wind and solar energy studies. Various MCDM techniques for decision making with their strengths and weaknesses were presented. Country specific studies which apply GIS-based method in site suitability were presented with criteria considered. Similarly, country-specific studies in ANFIS-based resource forecasts for wind and solar energy were also presented. From our findings, there has been no technically valid range of values for spatial criteria and the analytical hierarchical process (AHP) has been commonly used for criteria ranking leaving other techniques less explored. Also, hybrid ANFIS models are more effective compared to standalone ANFIS models in resource forecast, and ANFIS optimized with population-based models has been mostly used. Finally, we present a roadmap for integrating GIS-MCDM site suitability studies with ANFIS-based modeling for improved strategic and operational planning

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms

Solar radiation forecasting by Pearson correlation using LSTM neural network and ANFIS method: application in the west-central Jordan

none6siSolar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.Topical Collection "Computer Vision, Deep Learning and Machine Learning with Applications"openHossam Fraihat, Amneh A. Almbaideen, Abdullah Al-Odienat, Bassam Al-Naami, Roberto De Fazio, Paolo ViscontiFraihat, Hossam; Almbaideen, Amneh A.; Al-Odienat, Abdullah; Al-Naami, Bassam; DE FAZIO, Roberto; Visconti, Paol

Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan

Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations

Application of Predictive Models for Natural Gas Needs - Current State and Future Trends Review

Nowadays, in terms of trading on the world scale, to foresee a natural gas consumption represents an essential activity. In the first part, the paper examines the current state of the Serbian natural gas sector and methodology applied for prediction and capacity planning. In addition, the study intends to give a comprehensive assessment of predictive algorithms for natural gas needs involved in the last decade with projections and suggestions for future applications. The primary task is to evaluate used predictive models with an emphasis on the accuracy of the predictions obtained. Additionally, the paper will analyse used parameters, consumption scale, prediction scope, forecast algorithms, and other related information. The main objective of this study is to review the new-fangled information related analyses data from peer-reviewed journals, international conferences, and books

Wind generation forecasting methods and proliferation of artificial neural network:A review of five years research trend

To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method

Intelligent energy management system in buildings

Energy management systems have become one of the most significant concepts in the power energy area, due to the dependency of nowadays human’s lifestyle on electrical appliances and increment of energy demand during the past decades. From a general perspective, the total energy consumption by humans can be divided into three main economic sectors, namely industry, transportation, and buildings. Based on recent studies, the buildings present the largest share of consumption, standing for approximately 40% of the total consumption. This fact makes buildings energy management the most important component of energy management. On another hand, according to the variety of different types of buildings and several existing consumption appliances, the management of energy consumption in the building becomes a challenging problem. The main goal of a building energy management system is to control the energy consumption of the building by considering several facts, such as current and estimated consumption and generation, the energy price and comfort of the users. Due to the complexity of this management and limitations of available information, most of the existing systems focus on optimizing the consumption value and the cost of the energy with less consideration of the comforts and habits of the users. Moreover, the context of decision-making is also not sufficiently explored. However, the energy management in the building can be designed based on an intelligent system which has the knowledge to estimate the comforts and needs of the users and acts based on this awareness. This work studies and develops an intelligent energy management system for buildings energy consumption. This system receives the historical data of the building and uses a set of artificial intelligence techniques as well as several designed rulesets and acts as a recommender system. The goal of the generated recommendations by this system is to attune the usage of the electrical appliances of the building by comforts and habits of the residents while considering the price of the electricity market and the current context. Results show that the system enables users to obtain a comfortable environment in the building in the most affordable way.Nas últimas décadas, a dependência do estilo de vida na elevada utilização de dispositivos elétricos e grande consumo energético, faz com que os sistemas de gestão de energia sejam um dos conceitos mais relevantes no setor energético. Numa perspetiva geral, o total da energia consumida divide-se essencialmente em três setores económicos: industrial, transporte e edifícios. Os edifícios têm a maior representatividade, correspondendo aproximadamente a 40% do consumo total. Assim, a gestão energética em edifícios é a componente com maior importância nesta área. Por outro lado, devido à variedade dos diferentes tipos de edifícios e dispositivos de consumo, a gestão do consumo de energia nos edifícios apresenta desafios. O objetivo principal de um sistema de gestão energética em edifícios consiste em controlar o consumo energético no edifício, considerando diversos fatores, tais como o consumo e produção atuais, a sua estimativa, o preço de mercado e conforto dos seus utilizadores. Perante a complexidade desta gestão e das limitações da informação disponível, a maioria dos sistemas tem foco na otimização do consumo e os seus custos, tendo em menor consideração o conforto e hábito dos utilizadores. Além disso, o contexto da tomada de decisão não é devidamente explorado, enquanto a gestão energética em edifícios pode ser baseada num sistema inteligente, cujo conhecimento pode estimar o conforto e necessidades dos seus utilizadores, e assim atuar com base nessa consciência. Este trabalho estuda e desenvolve um sistema inteligente para a gestão do consumo de energia em edifícios. O sistema recebe o histórico de dados de um edifício, e utiliza um conjunto de técnicas de inteligência artificial e conjuntos de regras, funcionando como um sistema de recomendações. O objetivo das recomendações geradas pelo sistema é adaptar os dispositivos elétricos do edifício ao conforto e hábitos dos utilizadores enquanto são considerados o preço de mercado e o contexto atual. Os resultados demonstram que o sistema permite aos utilizadores obter um ambiente confortável no edifício, da forma mais económica possível

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators