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Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm

机译：基于新的最优LSTM-NN预测算法的短期电力负荷和价格预测

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摘要

Nowadays, a basic commodity for a human being to lead a standard lifestyle with human comfort irrespective of the nature of environmental conditions is electric power. The electricity load demand increases tremendously especially for a metropolitan city due to climatic conditions, population growth, local area development, industries expansion, air pollution, thermal device usage, etc. Hence, the accuracy of electricity load and its price forecasting is a deciding factor for the power distribution network to retain as an efficient, sustainable, and secure consumer-friendly network. On the other hand, based on the volatile, intermittent, and uncertain behavior of electricity load and price, an accurate, and robust forecast model should be designed. In this paper, a new hybrid forecast model for short-term electricity load and price prediction has been developed. The proposed method includes three modules: wavelet transform that is used to eliminate fluctuation behaviors of the electricity load and price time series, feature selection based on entropy and mutual information has been proposed to rank candidate inputs and eliminate redundant inputs according to their information value, and a new learning algorithm. The proposed learning method consists of a deep learning algorithm with LSTM networks which improves the accuracy of predictions. The performance of the proposed method has been validated successfully on load and price data collected from the Pennsylvania-New Jersey-Maryland (PJM) and Spain electricity markets. Also, for further test, the load data in Iran have been used.
机译：如今，人类的基本商品与人类舒适性带来标准的生活方式，无论环境条件的性质都是电力。由于气候条件，人口增长，局域开发，产业扩张，空气污染，热电设备使用等，尤其是大都市城市的电力负荷需求尤其增加。因此，电力负荷的准确性及其价格预测是一个决定性因素为了配电网络保留有效，可持续和安全的消费友好网络。另一方面，基于电力负荷和价格的挥发性，间歇性和不确定行为，应设计准确，更强大的预测模型。本文开发了一种新的短期电力负荷和价格预测的混合预测模型。该方法包括三个模块：小波变换用于消除电力负载和价格时间序列的波动行为，已经提出了基于熵和互信息的特征选择来排名候选输入并根据其信息值消除冗余输入，和一种新的学习算法。所提出的学习方法包括一种具有LSTM网络的深度学习算法，其提高了预测的准确性。拟议方法的性能已成功验证，从宾夕法尼亚州 - 新泽西州 - 马里兰州（PJM）和西班牙电力市场收集的负载和价格数据。此外，为了进一步测试，已经使用了伊朗的负载数据。

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