This paper performs a technoeconomic comparison of two hybrid renewable energy supplies (HRES) for a specific location in Ghana and suggests the optimal solution in terms of cost, energy generation capacity, and emissions. The two HRES considered in this paper were wind/hydrogen/fuel-cell and wind/battery storage, respectively. The necessity of this study was derived from the rise and expansion of hybrid renewable energy supply in a decentralised network. The readiness to embrace these new technologies is apparently high, but the best combination for a selected location that brings optimum benefits is not obvious and demands serious technical knowledge of their technical and economic models. In the methodology, an analytical model of energy generation by the various RE sources was first established, and data were collected about a rural-urban community in Doderkope, Ghana, to test the models. HOMER software was used to design the two hybrid systems based on the same load profiles, and results were compared. It turns out that the HRES 1 (wind/hydrogen/fuel-cell) had the lowest net present cost (NPC) and levelized cost of electricity (COE) over the project life span of 25 years. The energy reserve with the HRES 2 (wind/battery storage) was huge compared to that with the HRES 1, about 270% bigger. Furthermore, with respect to the emissions, the HRES 2 was environmentally friendlier than the HRES 1. Even though the battery storage seems to be more cost-effective than the hydrogen fuel-cell technology, the latter presents some merits regarding system capacity and emission that deserve greater attention as the world looks into more sustainable energy storage systems.

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 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.

The protection and maintenance of a power transmission system during fault condition is indispensable to ensure efficient and reliable power supply to consumers. Most methods of fault detection and location rely on measurements of electrical quantities provided by current and voltage transformers. In this paper, a prototype data collecting device was built for collecting data during different faulted conditions in a single-phase distribution network. Machine learning algorithms were developed for fault detection, location and classification on single-phase distribution lines. The transmission line was modelled using resistor network in the device; the current and voltage sensors were used in the prototype model with the data collection device for current and voltage readings under open-circuit and short-circuit faulted conditions. Training data was collected by varying the load on the line during the simulation of the fault type, sensor location on the node and analyzed. The test data was assessed using three (3) machine learning algorithms namely: K-Nearest Neighbor (KNN), Decision Trees and Support Vector Machines (SVM) for prediction of fault, location and classification within the single-phase distribution network. Test results showed that a higher accuracy rate of 99.42 % was obtained by using the Decision Trees algorithm compared to the others investigated.

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.

Photovoltaic Water Pumping System (PWPS) is a well known technology deployed in remote areas for the provision of drinking water and also for irrigation. Due to the high cost of solar energy implementation, the system becomes very costly. Unfortunately, innaccuracies in the system sizing mostly lead to oversizing, resulting in huge waste of money. This paper presents a mathematical method of sizing photovoltaic water pumping systems with more accuracy. The method starts with the assessment of losses through pipes and other accessories by using Poiseuil, Blasius and Blench laws. It also considers data on solar irradiation, average temperature as well as necessary parameters on the solar panel itself. It helps to calculate accurately the peak power that must be generated according to a demand. Moreover, simulations have been done in MATLAB to emphasize the effect of neglecting the temperature, the solar irradiation and the pipeline losses on the sizing method. Results show that big variations of temperature influence the sizing negatively, requiring more power than necessary; Bigger Solar irradiation requires fewer peak power. Again, hydraulic losses could add up to 10% of the necessary peak power.