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This paper assesses the performance of electricity generation using wind/hydrogen/fuel-cell technology. The intermittency of renewables, especially wind, and the need for storage of excess energy make them unattractive for continuous generation of electricity. This paper focuses on the wind resource of Anloga (Ghana) and the potential of hydrogen production from water electrolysis. The assessment of this system covers three main areas including the potential energy generation, environmental impacts, and economic impacts. The paper adopted analytical models of energy generation of fuel cell and hydrogen technologies and further performs their assessment using HOMER software. It was revealed that the annual electricity production from the hydrogen fuel cell is 25,999kW/yr, with an annual capacity shortage of 392kW/yr representing a 10% capacity shortage. The levelized cost of electricity was 0.602$/kWh and the emissions have been completely minimized as compared to diesel generation plants.

Even though many universities across the world have incorporated internet-based educational and academic systems, the success of their implementation requires an extensive understanding of the end user acceptance process Access to academic resources for teaching and learning using technology (electronic library) has become a popular approach within higher education institutions due to the continuous growth of internet innovations and technologies This case study research, conducted at Accra Technical University uses the technology acceptance model (TAM) as a theoretical framework The study investigates the effects of the system characteristics such as appropriate user interface (UI), software design, and relevance towards the perceived ease of use and perceived usefulness on intention to use the proposed e-library system Two hundred (200) students and sixteen (16) lecturers participated in this quantitative study Results show that improvement of the existing e-library system in ATU will enable students to utilize digital learning resources for effective teaching and learning, especially during the current global COVID-19 pandemic Copyright © 2021, IGI Global Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited

Globally, the superfluity of scholarly research conferences in varying disciplines has introduced the issue of scholarly big data and information overload related to both research papers and conference proceedings/sessions. This evident scholarly expansion in different disciplines has increased the collaborative importance of conferences. Consequently, the problem regarding attendees selecting the right conference session(s) to attend in academic conferences requires further and urgent attention. Using a smart conference scenario, this paper aims to address the problem above by proposing an improved venue recommender algorithm called Socially-Aware Recommendation of Venues and Environments-2 ( SARVE-2 ). Using a closeness centrality approach, SARVE-2 initially employs Breadth First Search (BFS) and Depth First Search (DFS) strategies to search for relevant presenters for a target attendee. Then, the tie strength of the (searched) presenter and target attendee is computed to generate reliable social (conference session) recommendations for the target attendee. Through the utilization of a relevant (real-world) dataset, our benchmark experiments reveal that, in comparison with other contemporary methods, SARVE-2 exhibits better performance in terms of effective social recommendation search, as well as social recommendation quality, coverage and accuracy.

Although in Accra Technical University (ATU), Ghana there exists a traditional face-to-face (F2F) mode of education already in place, the implementation of mobile learning (m-learning) through ICT in education will solve problems such as small classroom size, inappropriate time schedule for lectures, and provision technological resources needed to run successful classroom education. In order to validate successful implementation of m-learning in ATU, this paper employed a questionnaire research instrument with reference to the technology acceptance model (TAM-2) as a theoretical framework. Closed-ended questionnaires were administered to a sample size of 160 students and 15 lecturers in the Faculty of Applied Sciences (FAS) in ATU. Based on the responses received, the authors established positive technological acceptance of respondents, which paved the way to propose and develop a suitable m-learning system for ATU. It is envisaged that successful implementation of the m-learning system proposed in this paper will practically increase the use of ICT in education by both lecturers and students in ATU.

The rapid growth of mobile television (TV), smart TV, and Internet Protocol Television (IPTV) content due to the convergence of broadcasting and the Internet requires effective recommendation methods to select appropriate TV programs/channels. Many previous methods have been proposed to address this issue. However, imperative factors such as the utilization of personality traits and social properties to recommend programs for TV viewers remain a challenge. Consequently, in this paper, we propose a recommender algorithm called Recommendation of Programs via Personality and Social Awareness (ROPPSA) for TV viewers. ROPPSA utilizes normalization and folksonomy procedures to generate group recommendations for TV viewers who have common similarities in terms of personality traits and tie strength with a Target TV Viewer (TTV). Therefore, ROPPSA improves TV viewer cold-start and data sparsity situations by utilizing their personality traits and tie strengths. We conducted extensive experiments on a relevant dataset using standard evaluation metrics to substantiate our ROPPSA recommendation method. Results of our experimentation procedure depict the advantage, recommendation accuracy, and outperformance of ROPPSA in comparison with other contemporary methods in terms of precision, recall, f-measure (F1), and arithmetic mean (AM).

Nowadays, policymakers and researchers are discussing the relationship between internal migrations on poverty reduction. The uneven economic developments in Ghana these days, among others, have made the essential towns a des tination for internal migrants from other areas of the country. This study examines internal migration that acts as a way o ut of area poverty where conservative strategies on poverty reduction have failed to draw on empirical evidence on Ghana's civil movement. It is also examining how internal migration leads to economic development in rural areas. For the migrants a proportionate sample size of 680 used. Snowball sampling utilized for population selection (migrants) Gathered data from six regions and 38 districts in Ghana.It was analysed using questionnaires and interviews using versi on 22 of the Social Sciences Statistical Package (SPSS). The research also used statistical methods such as multinomial re gression, Spearman rank analysis of correlation, and ordinal model regression. Internal migration has a positive effect on poverty alleviation in Ghana bringing good drinking water supplies, electricity expansion, sanitation facilities, improved incomes, better healthcare, better nutrition, savings and investment opportunities to migrants, their families and communities among others. In order to maximize the effect of internal migration, the study recommended policy, theoretical and practical interventions on poverty reduction in Ghana

Although in Accra Technical University (ATU), Ghana, there exists a traditional Face-to-Face (F2F) mode of education already in place, the implementation of electronic learning (e-learning) through ICT in education will increase the number of students admitted yearly. Lack of academic facilities makes e-learning an attractive alternative. Consequently, this article employed a questionnaire as a research instrument with reference to the Technology Acceptance Model (TAM-2) as a theoretical framework. In all, questionnaires were administered to 190 students and 14 lecturers in the Faculty of Applied Sciences in Accra Technical University (ATU). Based on the responses received, various types of e-learning systems were exploited and a SWOT (Strength Weaknesses Opportunities and Threats) analysis was conducted to validate the sustainability of proposing and developing a suitable e-learning system for ATU. It is envisaged that successful implementation of the e-learning system proposed in this article will practically increase the use of ICT in education by both lecturers and students in ATU.

Distributed generation presents the potential solution to the power deficit problem faced in developing counties by encouraging local generation with renewable energy resources that are abundantly available in some part of the world, especially in Ghana. Distributed generations on the other hand are closely linked to Smart Grid (SG) that uses information technology to manage electricity networks. A plethora of problems relating to safety, reliability and affordability of Smart grid solutions remains challenging today. In Africa, especially in Ghana, the adoption of smart grid has been very slow, poorly managed, and almost inexistent in some countries despite the crucial need for electricity provision. This slow adoption can be attributed to many limiting factors that demand a thorough investigation. This paper aims at investigating the barriers to the adoption of smart grids in Africa and also develops and evaluates countermeasures to hasten the adoption process. A stratified sampling approach was used to collect comprehensive data in Ghana that were handled with SmartPLS software. The evaluation of factors was done through inferential statistics. The impact of the proposed measures was tested using a partial least structural equation model analysis. Findings revealed some factors that strongly influence the adoption of SG including cost, education, government policies; other factors like culture, Grid stability, had slight influence on the adoption process, nevertheless, societal perception had a low impact on the adoption process and therefore attracts less attention in fostering the SG acceptance in Ghana. Regarding SG prospects, the study identified several factors that can foster the prospect of smart grid adoption in Ghana namely education on smart grid technology, government policies and consumers behaviour. It is believed that, if careful attention is given to the factors listed above, the adoption of SG in Ghana will move at a faster speed.

This paper evaluates HVDS and LVDS concepts by system performance. This is achieved by examining system losses on HV and LV distribution networks in radial AC distribution systems. Challenges associated with system losses may demand network conversion from LV to HV network. This paper addresses this issue by using HVDS optimization specifically, linear programming techniques with Matlab optimization toolbox to determine the optimal number of unit transformers in the HV network. Studies conducted on a test distribution system using CYMEDIST software show the optimized HVDS has improved voltage profile and a total system loss reduction of 29.50% as compared to the LVDS. Consequently, there is reduced operational cost and increased annual capitalized loss savings in the optimized HVDS.

This research explores the trends of photovoltaic (PV) decentralization in Ghana and provides an updated illuminating insight into the sector. A literature review was carried out to understand and map out the trends, risks, and success factors. Additionally, time-series analysis was used to explore the installation trends from 2013 to 2019. A one-sample t-test was used to test the significance of each of the underlying indicators. The study identifies 2015 and 2018 as the years with the highest PV installations. On regional distribution, the research identified Central (63.25%), Greater Accra (26.44%), and Upper East (3.99%) as areas with the highest cumulative rate of a PV installation, respectively. This research raises awareness on the emerging perspectives on PV investment risk decisions and success criteria in Ghana. Considerations for potential investment opportunities and the stage of PV installations are expounded. It brings to light the geographical spread of installation that will aid critical policy decisions. Findings would be invaluable in making profound changes in the state’s policy formulation around PV. The study provides a sound basis for further research, to be directed towards complementing this work by focusing on the social impacts and further engagement of end-users with solar PV.

Accessibility to dependable energy resource is vital to the emerging economy to function appropriately in this contemporary world for both residential and commercial purposes. Technological advancement has opened avenues for more sophisticated technologies to combine multiple energy sources to generate affordable electricity for residential and industrial purposes. The study, therefore aims at analyzing the fiscal benefits of hybrid Solar PV and Diesel Generator (DG) (PV-DG) grid-connected system using RETSceen software. The study focused on a specific location, J. A. Plant Pool Ghana Limited, warehouse Department. The study recommended and proposed an effective design of a hybrid PV-DG grid connected electricity supply for the warehouse Department. The economic viability of the project and the payback period obtained through computation were very attractive. Also, the total revenue of electricity exported to the grid annually and GHG emission reduction were within the standard benchmark. The study finally concurred that renewable energy sources such as solar when hybridized with any other energy systems, lead to a noticeable electrical cost reduction. Therefore, this system design provides multiple benefits, including; improved reliability, reduced emission and significant cost reduction.

Hybrid power systems that combine wind and solar PV technology have been widely employed for power generation, particularly for electrification in remote and islanding locations, because they are more cost-effective and reliable than traditional power systems. This article intends to develop an environmentally friendly and cost-effective hybrid power system for selected critical loads in the Avuto community of Ghana. Following the acquisition of site data, a hybrid solar PV, wind, diesel generator, and converter analysis was conducted using HOMER software to establish the appropriate sizing of system components based on technical and economic parameters such as load served, annual electricity production, net present cost (NPC), emission, Operating cost, Fuel consumption and energy cost (COE). Based on the optimization computational results, it can be stated that the combination of system components, including solar photovoltaic, wind turbine, and diesel generator, is a good fit for the application region and might be used for rural and island electrification in the future. The suggested energy system has an LCOE of 0.39 US$/kWh for the 1.08 US$/litre diesel fuel cost and a 3.33-year payback period, with 58.8 kW for PV, 7 units for 3 kW wind turbines, 10 kW for diesel generator, and 6.99 kW for the converter. In terms of emission reduction, the proposed case presented a 55% emission reduction from the base case scenario.

Wind and Solar PV energy as alternative energy supplies to the traditional fossil fuel have been a subject of study for researchers at various fora including climate change summits. However, the technical and economic feasibility of wind and solar projects involve a lot of complexity and depends mostly on geographical location and availability of resources. To address the constraints and factors affecting wind and solar PV systems, this paper aims to undertake a techno-economic feasibility assessment of a grid connected solar PV/Wind hybrid system capable of meeting a typical commercial load, located in Kumasi, Ghana. To achieve the purpose of this study an energy audit has been undertaken to establish the load demand of the facility. Again, RETSCREEN software was used to design and simulate the proposed hybrid system in order to analyse the technical, economic and environmental implications of the system. Findings show that, there is a high potential for providing commercial scale energy consumption in most months in the year, and there is extra energy available to be sold to the grid, generating considerable income. Though the system has a high levelized cost of energy (LCOE) as compared to the existing grid tariff, the study adds significantly to the national objective to reduce its dependency on fossil fuels while meeting local energy requirement.

Access to energy is pivotal to the socio-economic growth of many developing countries, including Ghana. Energy generation from fossil fuels is not sustainable and leads to global warming, which is detrimental to the environment. This study seeks to establish how renewable energies are embedded and utilised in the Ghanaian energy system and the factors that can expedite the speedy penetration of renewable technologies into the country, particularly solar PV, wind, and biogas. The study adopted a mixed research approach which includes quantitative and qualitative studies. The findings revealed that solar energy is the most available resource in the country compared to other renewables. It was also indicative that integrating solar PV, wind and biogas in the national electricity grid will improve the percentage of energy generation mix, which will help sustain constant power supply, reduce the cost of energy charges, and consequently improve the country’s economy. The results also showed the possible factors that affect future penetration of these technologies, including unavailability of consumer financing opportunities, inadequate training facilities, lack of adequate regulations/policies, lack of information on the cost and benefits of renewable energies. The six findings of the paper established the availability of renewables in Ghana and the prospect of their related technology. While solar PV is on the ascendency, biogas is progressing gradually and wind is moving at a snail’s pace. This study significantly established the benefits of incorporating solar PV, wind and biogas in the Ghanaian energy mix to improve the electricity supply and further outlined the impeding factors that need to be improved upon through policy.

Globally, choosing the right tertiary programme for university (higher) education is quite a difficult task for students. A wide range of programmes are offered by the individual universities which differ in terms of delivery modes and entry requirements. Technology inclusion in the 21st century has paved the way for the proliferation of electronic/computing systems such as electronic counseling (e-counseling) and electronic learning (e-learning). By employing a quantitative research instrument (questionnaire) to ascertain technology acceptance of Senior High School (SHS) in Ghana, this chapter proposes a web-based (e-counseling) expert system which will match students' backgrounds with the right tertiary programme towards career development. Evaluation of our proposed approach suggests that majority of the selected students (80%) out of 100 who used the system accepted and embraced it. Such a system will therefore solve and improve career guidance, counseling, and development problems of SHS students in Ghana.

This paper presents the design and construction and control of a quadcopter drone for Aerial Data Collection (ADC). The frame of the drone was designed using CadDian Software and the parts were printed using a 3D printer. The flight controller was based on Arduino board using an Atmega328p microprocessor with GSM, GPS and GPRS for sending data over the internet and also enhancing long range flight. A feedback control system was developed and tested to control the stability of drone. The proposed control strategy of the drone was tested for a case of pursuit of trajectory and also for speed of response and the findings were very positive confirming the appropriateness of the control measures for independent and autonomous flying with promising precision. This Unmanned Aerial Vehicle (UAV) fitted with IoT has the capability of collecting and sending data over the internet and therefore can be used in many applications including risk assessment, forestry management, urban planning, coastal zone management, infrastructure monitoring, post-disaster damage assessment and delivery of medical supplies.

This paper seeks to propose the implementation of a mobile agriculture (m-agriculture) system for Poms Poultry Farm (PPF) to solve problems such as low patronage of farm products and customer relationship management among farmers and customers. To corroborate the successful implementation of an m-agriculture system at PPF, we employed a questionnaire research instrument concerning the Technology Acceptance Model (TAM) model as a theoretical framework. Closed-ended questionnaires were administered to a sample size of 130 PPF customers. Based on the responses received, we established optimistic responses towards technological acceptance which paved the way for us to propose and develop a suitable m-agriculture application for PPF. It is envisaged that successful implementation of our proposed system will practically increase productivity and help PPF reach out to their target customers.

Global energy saving efforts have led to replacement of incandescent lamps with energy-efficient ones like light-emitting diode (LED) and compact fluorescent lamps (CFLs). These lamps, being non-linear loads, have the potential of injecting harmonics into distribution networks. In this paper, harmonics injection of common CFL and LED lamps at a facility point of common coupling is investigated. To gain insight into large scale penetration effects on power quality, field measurement results of popular lamps used in Ghana were replicated in MATLAB/Simulink through simulation. The field results showed that LED lamps exhibit more harmonics compared to CFL lamps. Maximum possible loading on a 100-kVA, 11kV/0.433kV distribution transformer was found to be 24.02% for CFL, 27.14% for LED, and 40.91% for a mixture of the two lamps, respectively, in order not to violate IEEE 519-2014 standard. The influence of other common loads such as ceiling fans on the lamps’ harmonics were assessed in the field measurement. The use of ceiling fans with the lamps in the facility reduced the harmonics and improved the power factor of the facility. Since the lamps exist in residential and commercial facilities with other loads, more penetration of energy-saving lamps in the distribution system will have little influence on power quality. Keywords : Compact fluorescent lamps, light emitting diodes, maximum power loading, total harmonic distortion, point of common coupling

Globally, the current coronavirus disease 2019 (COVID-19) pandemic is resulting in high fatality rates. Consequently, the prevention of further transmission is very vital. Until vaccines are widely available, the only available infection prevention methods include the following: contact tracing, case isolation and quarantine, social (physical) distancing, and hygiene measures (washing of hands with soap and water and using alcohol-based hand sanitizers). Contact tracing, which is key in preventing the spread of COVID-19, refers to the process of finding unreported people who maybe infected by using a verified case to trace back possible infections of contacts. Consequently, the wide and fast spread of COVID-19 requires computational approaches which utilize innovative algorithms that build a memory of proximity contacts of cases that are positive. In this paper, a recommender algorithm called socially aware recommendation of people probably infected with COVID-19 (SARPPIC) is proposed. SARPPIC initially utilizes betweenness centrality in a social network to measure the number of target contact points (nodes/users) who have come into contact with an infected contact point (COVID-19 patient). Then, using contact durations and contact frequencies, tie strengths of the same contact points above are also computed. Finally, the above algorithmic computations are hybridized through profile integration to generate results for effective contact tracing recommendations of possible COVID-19-infected patients who will require testing in a healthcare facility. Benchmarking experimental results in the paper demonstrate that, using two interconnected relevant real-world datasets, SARPPIC outperforms other relevant methods in terms of suitable evaluation metrics such as precision, recall, and F-measure.

This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.

In order to innovatively solve cold-start problems, research involving trust and socially aware recommender systems is currently proliferating. The relative importance of academic conferences has led to the necessity of recommender systems that seek to generate recommendations for conference attendees. In this paper, we aim to improve the recommendation accuracy of socially-aware recommender systems by proposing a linear hybrid recommender algorithm called Personality and Socially-Aware Recommender ( PerSAR ). PerSAR hybridizes the social and personality behaviours of smart conference attendees. Our recommendation methodology mainly aims to employ an algorithmic framework that computes the personality similarities and tie strengths of conference attendees so that effective and reliable recommendations can be generated for them using a relevant dataset. The experimental results substantiate that our proposed recommendation method is favorable and outperforms other related and contemporary recommendation methods and techniques.