Nigeria Generating Station Capacity and Load Demand Requirement Using Regression Analysis (Least Square) and Artificial Neuro-Fuzzy Inference System (Anfis) for Reliable Power Supply - Braide, Sepiribo Lucky (European Journal of Applied Sciences, 2022)
The increasing demand of electrical energy is growing exponential rate while there is mismatched between power supply and power demand. This means that there is strong record to determine the existing state of the system for purpose of future projection of power generation to avoid system collapse. Evidently, to conduct the analysis of system forecast for energy demand, many mathematical techniques and artificial machine learning which is based on artificial intelligence. This paper considered the application of least – square regression technique and artificial neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) principle, on the view to train the data set for Nigeria energy consumption (residential. Commercial and industrial) on the view to determine total system consumption pattern for reliable power supply. The data used ranges from (2000-2012) as input for the projection plan of 2013-2035, was determined for Nigeria power system. This is modelled and simulated in matlab software tool (version 19.0). the purposed artificial intelligence system based on Artificial neural fuzzy inference system (ANFIS) which provided high machine learning fixtures to predicts close to the actual value as compared to the traditional least square algorithms. The results were evaluated using mean absolute percentage error (MAPE) of 5.73%. The proposed technique ANFIS predicted load forecast (2013-2035) to provide energy data for electric utilities and to plan successfully for efficient power generation. Application of Adaptive Neuron – fuzzy inference in artificial intelligence involved both neural network (NN) and fuzzy logic (FL) principle together an adaptive neuro-fuzzy inference system (ANFIS), is an artificial neural network that is based on a fuzzy inference system. ANFIS is a very useful system to extract numerical model from numerical data since it integrates both neural network and fuzzy logic principle together, it is capable of adapting the benefits of both in a single framework. An adaptive neuro-fuzzy inference system (ANFIS) is an application of adaptive neuro-fuzzy logic that uses frame work of artificial intelligence (AI). Hence its inference system corresponds to a code of IF – THEN RULE in fuzzy neuro network. It has a learning capability to approximate non-linear functions hence adaptive neurofuzzy inference system (ANFIS) deserves to be considered as good estimator.