A dependable design and monitoring of solar energy-based systems necessitates precise data on available solar radiation. However, measuring solar radiation is challenging due to the expensive equipment required for measurement, along with the costs of calibration and maintenance, especially in developing countries like Nigeria. As a result, data-driven techniques are often employed to predict solar radiation in such regions. However, the existing predictive models frequently yield unsatisfactory outcomes. To address this issue, this study proposes the creation of intelligent models to forecast solar radiation in Kano state, Nigeria. The model is developed using an ensemble machine learning approach that combines two Adaptive Neuro-Fuzzy Inference Systems with sub-clustering optimization and grid-partitioning optimization. The meteorological data used for model development include maximum temperature, minimum temperature, mean temperature, and solar radiation from the previous 2 days as predictors. To evaluate the models performance, various metrics like correlation coefficient, determination coefficient, mean-squared error, root-mean-squared error, and mean-absolute error are employed. The simulation results demonstrate that the ANFIS ensemble outperforms the individual ANFIS models. Notably, the ANFIS-ENS exhibits the highest accuracy. Consequently, the developed models provide a reliable alternative for estimating solar radiation in Kano and can be instrumental in enhancing the design and management of solar energy systems in the region.

This paper presents the potential of generating wind power using soft computing model and ground station data. In reality, the process of wind resource assessment is to set up an experiment in the targeted locations, and measure the wind speed and direction. In this paper, a prediction model based on the terrain based neural network named terrain nonlinear autoregressive neural network (TNARX) is proposed to forecast the wind speed in the areas not covered by measurements using a ground station located nearby. The model has meteorological, physical and topographical as input, while the wind speed is the target variable. The suitability of the proposed model was judged using statistical measures. The paper shows characteristics of wind speed and the most prevailing wind directions. The variation of wind speed at 10–40 m heights was obtained and presented. Wind speed distribution modelling was carried out using five statistical models. It was found that Weibull and Gamma fits the wind speed of the studied areas. Wind power and energy density results show the areas falls within class 1, which is possible for harnessing energy content in wind for small scale purposes.

Clean energy generation using groundnut oil mill effluent with microbial fuel-cell

Accurate and precise wind speed predictions are a prerequisite requirement that isnecessary before siting of wind turbines. The output power of wind energy system iscompletely depends on the behavior of wind speed; a small deviation of wind speed willlead to large energy losses. This paper presents a new technique for predicting the windspeed based on hybrid model Intrinsic Mode Function (IMF) and Extended Input NeuralNetwork (EINN) in the regions where there are limited wind stations. In the first instant,the important parameters for training the artificial neural network (ANN) are acquiredusing the principal component correlation analysis and wind speed signal decomposition,these parameters used as inputs to the ENN. To illustrate the trend and seasonal factor inthe wind speed time series, the data are decomposed into six empirical time series IMF, thenonlinear and non- stationary characteristic of wind speed is handled by empirical modedecomposition (EMD) and EINN respectively. The final predicted values are obtained bysumming all the individual prediction sub models. Wind speed data observed in theexisting wind stations in Sarawak for a period of 1 year from 2015 to 2016 were used forthe simulation. The model implementation confirmed that the proposed model is robust andcapable compared to auto-regression integrated moving average (ARIMA) method.

The paper does not look sound and the followings are needed CONDUCT A COMPREHENSIVE LITERATURE REVIEW: Ensure that your literature review covers all relevant and up-to-date sources, including academic journals, books, and conference proceedings. Sy...