A hybrid PSO-ANFIS approach for horizontal solar radiation prediction in Nigeria

For efficient and reliable hydrogen production via solar photovoltaic system, it is important to obtain accurate solar radiation data. Though there are equipment specifically designed for solar radiation prediction but are very expensive and have high maintenance cost that most countries like Nigeria are unable to purchase. In this study, the accuracy of a hybrid PSO-ANFIS method is examined to predict horizontal solar radiation in Nigeria. The prediction is done based on the available meteorological data obtained from NIMET Nigeria. The meteorological data used for this study are monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours, which serves as inputs to the developed model. The model accuracy is evaluated using two statistical indicators Root Mean Square Error (RMSE) and Coefficient of determination (R²). The accuracy of the proposed model is validated using ANFIS, GA-ANFIS models and other literatures. Based on the statistical parameters used for the model evaluation, the results obtained proves PSO-ANFIS as a good model for predicting solar radiation with the values of RMSE=0.68318, R²=0.9065 at the training stage and RMSE=1.3838, R²=0.8058 at the testing stage. This proves the potentiality of PSO-ANFIS technique for accurate solar radiation prediction

Penalization of electricity thefts in smart utility networks by a cost estimation-based forced corrective measure

Electricity theft menace has attracted various research efforts with most proposed detection algorithms relying on analysing customers' consumption profile to determine fraudulent electricity consumers (FEC). This necessitates the need for on-site inspections before penalties are sanctioned despite the manpower, cost, energy, time, and stress associated with such tedious routine. Moreover, the penalty-imposed fines are bogusly determined and uncoordinated, and losses in revenue are burdened on the honest consumers. Fortunately, the advent of advanced metering infrastructure offers a flexible and efficient platform which can be leveraged to provide additional functionality of curbing these complicated procedures. In this work, a cost estimation-based model deploying a forced corrective measure for a real-time enforcement of penalties on FEC in a smart utility network is proposed. It relies on the results of commonly applied intelligent algorithms for electricity theft detection to obtain the amount and cost of energy consumed by reported FEC while also providing efficient monitoring till imposed fines are cleared. The results of the developed model give proportionate sanctions and enhances the functions of the system manager's monitoring of the operational status to ensure compliance and is suitable for deployment in a smart utility network