Short-Term Forecasting of Load and Renewable Energy Using Artificial Neural Network
2020
Load forecasting is a technique used for the prediction of electrical load demands in battery management. In general, the
aggregated level used for Short-Term Electrical Load Forecasting (STLF) consists of either numerical or non-numerical
information collected from multiple sources, which helps in obtaining accurate data and efficient forecasting. However,
the aggregated level cannot precisely forecast the validation and testing phases of numerical data, including the real-time
measurements of irradiance level (W/m2) and photovoltaic output power (W). Forecasting is also a challenge due to the
fluctuations caused by the random usage of appliances in the existing weekly, diurnal, and annual cycle load data. In this study,
we have overcome this challenge by using Artificial Neural Network (ANN) methods such as Bayesian Regularization (BR) and
Levenberg–Marquardt (LM) algorithms. The STLF achieved by ANN-based methods can improve the forecast accuracy. The
overall performance of the BR and LM algorithms were analyzed during the development phases of the ANN. The input layer,
hidden layer and output layer used to train and test the ANN together predict the 24-hour electricity demand. The results
show that utilizing the LM and BR algorithms delivers a highly efficient architecture for renewable power estimation demand.
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