Efficient Energy Planning With Decomposition-Based Evolutionary Neural Networks

Tanveer Ahmad,Wahab Ali Shah,Dongdong Zhang

doi:10.1109/access.2020.3010782

Abstract

Load forecasting/prediction is a challenging task in the energy markets that require adequate attention in generating stable and reliable load demand to deal with energy management and planning strategies. Accurate load prediction is critical for electrical power systems operations, but nonlinear loads involve high volatility. Forecasting these kinds of complex load characteristics requires highly accurate forecasting devices. It is generally accomplished by constructing models on relative details, including weather and previous data on load demand. This study proposes six decomposition-based evolutionary neural networks for city-scale and building energy forecasting. These evolutionary neural networks have also been trained using historical load data, and weather records are already considered to have a significant effect on energy usage (for example, wind speed, precipitation, dry-dew point temperature, relative humidity, clouds fraction and mean sea level perception). The network structure of the model is built and based on error estimation, trend map, and appropriate method of measuring evolutionary neural networks efficiency. The model's hidden layers and neurons network structure is selected based on recent smart models to enhance its accuracy. Several measures were used to improve performance, such as seasonal smoothing changes, coarse-graining of the humidity ratio, load-oriented day-type classification monitoring, outlier removal, and complex climate information. Results show that used models have a very high fitting accuracy and low error rate for short, medium, and long-term forecasting/planning tasks. These models have the potential to reduce overfitting issues, thereby improving load forecasting efficiency. Further, proposed models may also use for forecasting solar and wind demand. The efficiency of the model compared to the existing two models. Forecast results show superiority and efficiency in short, medium and long-term energy forecasting and offer the ability to manage unbalanced utility loads.

Highlights

Energy systems’ distribution infrastructure and their realtime operations are growing increasingly worldwide, with a traditional drift of change in energy quality, carbon pollution reduction, and energy management
It concerns the fact that Part-II is a better predictive performance of RBNN, LANN, CFNN, PB-CBNN, GDMALBNN and L-RNN compared to Part-I
In this study, the evolutionary-based six artificial neural networks (ANNs) models used with a variety of variable features for large-scale utilities and building heating and cooling load planning and management

Summary

Introduction

Energy systems’ distribution infrastructure and their realtime operations are growing increasingly worldwide, with a traditional drift of change in energy quality, carbon pollution reduction, and energy management. A higher distribution of energy operations connecting conventional and renewable energy production would be an integral part of the future. Grid, offering both thermal and electricity cooperation at the energy consumption level— residential, town, commercial and industrial consumers [1]. Small-scale grids cover the large area of district-level electricity distribution and could be classified as each stand-alone, grid-connected, or useby-residential, military, commercial, etc. Potential energy sources, most of them focused on photovoltaic cells, fossil fuel generators and wind turbines [2]–[4]. The performance estimation of stand-alone and hybrid renewable energy networks [2], renewable heating strategies, smart.

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Results

Conclusion