Short-Term Forecasting of Load and Renewable Energy Using Artifical Neural Network

Short-Term Forecasting of Load and Renewable Energy Using Artifical Neural Network
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© 2021 by IJETT Journal
Volume-69 Issue-6
Year of Publication : 2021
Authors : Ram Srinivasan, Venki Balasubramanian, Buvana Selvaraj
DOI :  10.14445/22315381/IJETT-V69I6P226

How to Cite?

Ram Srinivasan, Venki Balasubramanian, Buvana Selvaraj, "Short-Term Forecasting of Load and Renewable Energy Using Artifical Neural Network," International Journal of Engineering Trends and Technology, vol. 69, no. 6, pp. 175-181, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I6P226

Abstract
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 Regularisation (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.

Keywords
Forecasting, Accuracy, Short-term load forecasting, Artificial neural network, Wind power, PV power, Electrical load, Aggregated level.

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