Optimal planning and sizing of an autonomous hybrid energy system using multi stage grey wolf optimization

The continuous increase in energy demand and the perpetual dwindling of fossil fuel coupled with its environmental impact have recently attracted research focus in harnessing renewable energy sources (RES) across the globe. Representing the largest RES, solar and wind energy systems are expanding due to the growing evidence of global warming phenomena. However, variability and intermittency are some of the main features that characterize these RES as a result of fluctuation in weather conditions. Hybridization of multiple sources improves the system’s efficiency and reliability of supply due to the varying nature of the RES. Also, the unavailability of solar radiation (SR) and wind speed (WS) measuring equipment in the meteorological stations necessitates the development of prediction algorithms based on Artificial Intelligent (AI) techniques. This thesis presents an autonomous hybrid renewable energy system for a remote community. The hybrid energy system comprises of a photovoltaic module and wind turbine as the main source of energy. Batteries are used as the energy storage devices and diesel generator as a backup energy supply. A new hybrid Wavelet Transform and Adaptive Neuro-Fuzzy Inference System (WT-ANFIS) is developed for the SR prediction, while a hybrid Particle Swarm Optimization (PSO) and ANFIS (PSO-ANFIS) algorithm is developed for the WS prediction. The prediction accuracy of the proposed WT-ANFIS model was validated by comparison with the conventional ANFIS model, Genetic Algorithm (GA) and ANFIS (GA-ANFIS), and PSO-ANFIS models. The proposed PSO-ANFIS for the WS prediction is also compared with ANFIS and GA-ANFIS models. Also, Root Mean Square Error (RMSE), Correlation Coefficient (r) and Coefficient of Determination (R²) are used as statistical indicators to evaluate the performance of the developed prediction models. Additionally, a techno-economic feasibility analysis is carried out using the SR and WS data predicted to assess the viability of the hybrid solar-wind-battery-diesel system for electricity generation in the selected study area. Finally, a new cost-effective Multi Stage – Grey Wolf Optimization (MS-GWO) algorithm is applied to optimally size the different system components. This is aimed at minimizing the net present cost (NPC) while considering reliability and satisfying the load demand. MS-GWO is evaluated by comparison with PSO, GWO and PSO-GWO algorithms. From the results obtained, the statistical evaluators used for model performance assessment of the SR prediction shows that the hybrid WT-ANFIS model’s accuracy outperforms the PSO-ANFIS model by 65% RMSE and 9% R². Also, from the simulation results, the optimal configuration has an NPC of $1.01 million and cost of energy (COE)$0.110/kWh, with an operating cost of $4,723. The system is environmentally friendly with a renewable fraction of 98.3% and greenhouse gas emission reduction of 65%. Finally, a comparison is done between the proposed MS-GWO algorithm with the PSO, GWO and PSO-GWO algorithms. Based on this comparison, the proposed hybrid MS-GWO algorithm outperforms the individual PSO, GWO and PSO-GWO by 3.17%, 2.53% and 2.11% in terms of NPC and reduces the computational time by 53%, 46% and 36% respectively. Therefore, it can be concluded that the proposed MS-GWO technique can be applied for optimal sizing application globally

Peer-to-peer Approach for Distributed Privacy-preserving Deep Learning

The revolutionary advances in machine learning and Artificial Intelligence have enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making. Deep learning is the most effective, supervised, time and cost efficient machine learning approach which is becoming popular in building today’s applications such as self-driving cars, medical diagnosis systems, automatic speech recognition, machine translation, text-to-speech conversion and many others. On the other hand the success of deep learning among others depends on large volume of data available for training the model. Depending on the domain of application, the data needed for training the model may contain sensitive and private information whose privacy needs to be preserved. One of the challenges that need to be address in deep learning is how to ensure that the privacy of training data is preserved without sacrificing the accuracy of the model. In this work, we propose, design and implement a decentralized deep learning system using peer-to-peer architecture that enables multiple data owners to jointly train deep learning models without disclosing their training data to one another and at the same time benefit from each other’s dataset through exchanging model parameters during the training. We implemented our approach using two popular deep learning frameworks namely Keras and TensorFlow. We evaluated our approach on two popular datasets in deep learning community namely MNIST and Fashion-MNIST datasets. Using our approach, we were able to train models whose accuracy is relatively close to models trained under privacy-violating setting, while at the same time preserving the privacy of the training data

Effect of Pressure on Structural, Elastic and Electronic Properties of Perovskite PbTiO3

We study the effect of pressure on Structural, elastic and electronic properties of Cubic and Tetragonal Perovskite using density function theory. The equilibrium parameters obtained are in good agreement with the available literature both experimental and theoretical. We found out that there is transition from tetragonal to cubic at a pressure of around 30GPa. Both crystals are stable in the pressure range of this study (0 – 50 GPa), and the stability increases with increasing pressure. The bulk modulus (B), Young modulus (E) and Shear modulus (G) all increase with increasing pressure. The band-gap increases and decrease around (X-Gamma) and (M-Gamma) for the case of Cubic and decrease for the case of Tetragonal Crystal around (X-Gamma), (Z-Gamma) and (Z-X) which converges at pressure of around 30GPa

Solar radiation forecasting in nigeria based on hybrid PSO-ANFIS and WT-ANFIS approach

For an effective and reliable solar energy production, there is need for precise solar radiation knowledge. In this study, two hybrid approaches are investigated for horizontal solar radiation prediction in Nigeria. These approaches combine an Adaptive Neuro-fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) and Wavelet Transform (WT) algorithms. Meteorological data comprising of monthly mean sunshine hours (SH), relative humidity (RH), minimum temperature (Tmin) and maximum temperature (Tmax) ranging from 2002-2012 were utilized for the forecasting. Based on the statistical evaluators used for performance evaluation which are the root mean square error and the coefficient of determination (RMSE and R²), the two models were found to be very worthy models for solar radiation forecasting. The statistical indicators show that the hybrid WT-ANFIS model’s accuracy outperforms the PSO-ANFIS model by 65% RMSE and 9% R². The results show that hybridizing the ANFIS by PSO and WT algorithms is efficient for solar radiation forecasting even though the hybrid WT-ANFIS gives more accurate results

Proposal for Ontology Based Approach to Fuzzy Student Model Design

Abstract -Intelligent tutoring system (ITS) is a software system designed using artificial intelligent techniques (comprising of Fuzzy Logic, Neural-Networks, Bayesian networks, Ontology, Genetic Algorithms and Software Agents) to provide an adaptive and personalized tutoring suitable to each individual student based on his/her profile or characteristics. In this paper we intend to employ the use of Fuzzy logic and Ontology techniques to model the student's learning behaviour with the aim of improving the learning path and increase the system's adaptability. The use of fuzzy logic in this context is to enable the computational analysis of the student's characteristics and learning behaviours in order to handle the uncertainty issues related to the student model design. Ontology is a vital tool for managing knowledge in a particular domain and is one of the recent techniques used to design the representation of student's cognitive state

Threats and challenges of smart grids deployments - a developing nations’ perspective

Considerable efforts in huge investments are being made to achieve a resilient Smart Grids (SGs) deployment for the improvement of power delivery scheme. Unsurprisingly, many developing nations are making slow progress to the achievement of this feat, which is set to revolutionize the power industry, own to several deployment and security issues. Studying these threats and challenges from both technical and non-technical view, this paper presents a highlight and assessment of each of the identified challenges. These challenges are assessed by exposing the security and deployment related threats while suggesting ways of tackling these challenges with prominence to developing nations. Although, a brief highlight, this review will help key actors in the region to identify the related challenges and it’s a guide to sustainable deployments of SGs in developing nations

A fuzzy logic approach to manage uncertainty and improve the prediction accuracy in student model design

The intelligent tutoring systems (ITSs) are special classes of e-learning systems developed using artificial intelligent (AI) techniques to provide adaptive and personalized tutoring based on the individuality of each student. For an intelligent tutoring system to provide an interactive and adaptive assistance to students, it is important that the system knows something about the current knowledge state of each student and what learning goal he/she is trying to achieve. In other words, the ITS needs to perform two important tasks, to investigate and find out what knowledge the student has and at the same time make a plan to identify what learning objective the student intends to achieve at the end of a learning session. Both of these processes are modeling tasks that involve high level of uncertainty especially in situations where students are made to follow different reasoning paths and are not allowed to express the outcome of those reasoning in an explicit manner. The main goal of this paper is to employ the use Fuzzy logic technique as an effective and sound computational intelligence formalism to handle reasoning under uncertainty which is one major issue of great concern in student model design

Prevalence of Mosquitoes in Gidan Yunfa Community of Usmanu Danfodiyo University, Sokoto, Nigeria

Different species of mosquito serves as a vector for transmitting malaria. Malaria is still a serious public health problem in Nigeria. Knowledge of the mosquito species, their diversity, and their composition would help immensely toward proper implementation of the different control strategies. This study was carried out to determine the prevalence of mosquitoes and feeding or biting period in Gidan Yunfa community of Usmanu Danfodiyo University, Sokoto, Nigeria. The Larvae and Pupae were collected from breeding sites. Adult mosquitoes were sampled using CDC light traps (situated indoor and outdoor) and Pyrethrum Spray Catch methods. Mosquitoes were identified morphologically. A total of 6,410 adult mosquitoes with 2,142 (33.42 %) obtained from CDC light traps and 4,268 (66.58%) from the larval collections were identified belonging to 3 genera Aedes, Anopheles, and Culex. A maximum number of mosquitoes were caught with CDC traps. The abundance of the different genera varied significantly (P<0.05) with Anopheles having the highest occurrence (54.75%) followed by Culex mosquitoes with 40.42%. Aedes has the least abundance with 8.05%. The indoor and outdoor feeding habits of the different species varied significantly (P<0.05). Nature of the houses and tethering of animal in residential houses and abundance of breeding places may explain the reason behind the higher prevalence of the mosquito in this community

Computational intelligence approaches for student/tutor modelling: a review

The intelligent tutoring system (ITS) is an educational software system that provides personalized and adaptive tutoring to students based on their needs, profiles and preferences. The tutor model and student model are two dependent components of any ITS system. The goal of any ITS system is to help the students to achieve maximum learning gain and improve their engagements to the systems by capturing the student's interests through the system's adaptive behavior. In other words an ITS system is always developed with the aim of providing an immediate and efficient solution to student's learning problems. In recent years a lot of work has been devoted to improving student and tutor models in order enhance the teaching and learning activities within the ITS systems. The aim of this paper is to investigate the most recent state of art in the development of these two vital components of the intelligent tutoring systems