Artificial Neural Network and Particle Swarm Optimization for Medium Term Electrical Load Forecasting in a Smart Campus

Energy demand has continued to increase rapidly not exempting Covenant University. As the university continues to witness infrastructural expansion and population increase, it has become a necessity for energy consumption to be predicted. Hence, this research work developed a medium-term load forecasting system to solve this problem and ensure an efficient electricity supply from the power system operators of Covenant University. The forecast was carried out on real-time monthly load data collected from the university community power plant between 2015 and 2018, using the Artificial Neural Network (ANN) model. A medium-term load forecast was evaluated based on three different ANN algorithms. The FeedForwardNet, Cascadeforwardnet and Fitnet are tested against three (3) different learning algorithms namely Levenberg Marquardt, Bayesian regularization and BFGS quasi-Newton backpropagation with a particle swarm optimizer. And the network performance was obtained using Normalized Root Mean Square Error (nRMSE %). The result revealed an nRMSE of 0.0634%, a correlation coefficient, r, of 0.9082 and the fastest computation speed of 171.789 seconds. Hence, this study provides a point of reference for other related studies and future energy forecast improvement in the study location

An optimized fractional grey model based on weighted least squares and its application

The fractional grey model is an effective tool for modeling small samples of data. Due to its essential characteristics of mathematical modeling, it has attracted considerable interest from scholars. A number of compelling methods have been proposed by many scholars in order to improve the accuracy and extend the scope of the application of the model. Examples include initial value optimization, order optimization, etc. The weighted least squares approach is used in this paper in order to enhance the model's accuracy. The first step in this study is to develop a novel fractional prediction model based on weighted least squares operators. Thereafter, the accumulative order of the proposed model is determined, and the stability of the optimization algorithm is assessed. Lastly, three actual cases are presented to verify the validity of the model, and the error variance of the model is further explored. Based on the results, the proposed model is more accurate than the comparison models, and it can be applied to real-world situations

An optimal fractional-order accumulative Grey Markov model with variable parameters and its application in total energy consumption

In this paper, we propose an optimal fractional-order accumulative Grey Markov model with variable parameters (FOGMKM (1, 1)) to predict the annual total energy consumption in China and improve the accuracy of energy consumption forecasting. The new model is built upon the traditional Grey model and utilized matrix perturbation theory to study the natural and response characteristics of a system when the structural parameters change slightly. The particle swarm optimization algorithm (PSO) is used to determine the number of optimal fractional order and nonlinear parameters. An experiment is conducted to validate the high prediction accuracy of the FOGMKM (1, 1) model, with mean absolute percentage error (MAPE) and root mean square error (RMSE) values of 0.51% and 1886.6, respectively, and corresponding fitting values of 0.92% and 6108.8. These results demonstrate the superior fitting performance of the FOGMKM (1, 1) model when compared to other six competitive models, including GM (1, 1), ARIMA, Linear, FAONGBM (1, 1), FGM (1, 1) and FOGM (1, 1). Our study provides a scientific basis and technical references for further research in the finance as well as energy fields and can serve well for energy market benchmark research