Deep learning for multi-scale smart energy forecasting

Short-term load prediction at the district-level is essential for feeders, substations, consumers and transformers starts from 1-h to one-week ahead. Though, the critical problem is in the design of complex load characteristics, switching operations and a large number of junctions at electrical distribution feeders. To overcome the design challenges, this research conducted an analysis of short-term energy requirement forecasting for district-level by applying into two deep learning models. Present, the booming increase of deep learning (DL) methods give them encouraging options to data-driven techniques. However, DL gives exceptional ability in handling the non-linear complex relations, model computation performance and complexity are of attention. Further, the two different climate zones data used for modeling analysis. The input feature parameter sets classified into two parts: i) features selection-I (FS-I); and FS-II. The FS-1 comprises based on seven environmental parameters, including various limits of energy consumption data sets and the FS-ll contains the different sixteen feature variables. The FS-II mean absolute error is <2.05% and <1.577% of OSSB-NN and BFGS-QNB respectively. The deep learning models achieved higher forecasting accuracy at the proposed structure of the model's network at different hidden neurons. Based on the current trend and the comparison results, a structure of district energy prediction and future research direction proposed for district-level design, planning, and services. (C) 2019 Elsevier Ltd. All rights reserved.