Improving Load Forecasting Process for a Power Distribution Network Using Hybrid AI and Deep Learning Algorithms

Abstract

Load forecasting is useful for various applications, including maintenance planning. The study of load forecasting using recent state-of-the-art hybrid artificial intelligence (AI) and deep learning (DL) techniques is limited in South Africa (SA) and South African power distribution networks. This paper proposes a novel hybrid AI and DL South African distribution network load forecasting system. The system comprises of modules that handle the collection of the loading data from the field, analysis of data integrity using fuzzy logic, data preprocessing, consolidation of the loading and the temperature data, and load forecasting. The load forecasting results are then used to inform maintenance planning. The load forecasting is conducted using a hybrid AI/DL load forecasting module. A novel comparative study of recent state-of-theart AI techniques is also presented to determine the best technique to deploy in this module when forecasting South African power redistributing customers' loads. The impact of the inclusion of weather parameters and loading data clean up on the load forecasting performance of a hybrid AI technique, optimally pruned extreme learning machines (OP-ELM), and a deep learning technique, long short-term memory (LSTM), is also investigated. These techniques are compared with each other and also with a commonly used powerful hybrid AI technique, adaptive neuro-fuzzy inference system (ANFIS). LSTM was found to achieve higher load forecasting accuracies than ANFIS and OP-ELM in forecasting the two distribution customers' loads in this paper. Only the LSTM models' performance improved with the inclusion of temperature in their development.INDEX TERMS Adaptive neuro-fuzzy inference systems, artificial intelligence, deep learning, distribution networks, extreme learning machines, load forecasting, recurrent neural networks, long short-term memory.

An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

“…The SGTM neural-like structure showed a better prediction performance than MLR, SVR, and general regression neural networks. Motepe et al [ 17 ] developed an improved load forecasting process using hybrid AI and deep learning methods. They determined the optimal hyperparameters through several experiments to enhance the performance of long short-term memory (LSTM) networks.…”

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

“…To mitigate this challenge and reduce the errors of the forecasting of the electric parameters, DBN is combined with Copula Model [27] and bidirectional Recurrent Neural Network [28], [29] for day and week ahead forecasting. An LSTM based hybrid DL method was tested by Motepe et al [30] for South African distribution network, showing an improved performance, considering the inclusion of temperature data. Deng et al [31] devised a deep multi-scale CNN (MSCNN) with time cognition and a selfdesigned time coding algorithm, which outperformed recursive multi-step LSTM, direct multi-step MSCNN and the direct multi-step gated CNN by MAPE of 34.73%, 14.22% and 19.05% respectively.…”

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

“…WNN (Wavelet Neural Network) with BPNN has been used in MTLF to determine the reduction of the unavoidable stochastic part [41]. Neuro-fuzzy is a fuzzy logic-based ANN technique and has been applied to RNN, LSTM, ELM (Extreme Learning Machine) in STLF [91], and other ANNs for improving some issues in load forecasting. After reviewing 105 relevant deep learning-based load forecasting research works, it was found that STLF was the most important forecasting area, with 88 works on STLF.…”