Application of ALO-ELM in Load Forecasting Based on Big Data

The load of power system changes with the development of economy, short-term load forecasting play a very important role in dispatching and management of power system. In this paper, the Ant Lion Optimizer (ALO) is introduced to improve the input weights and hidden-layer Matrix of extreme learning machine (ELM), after the parameters of ELM are optimized by ALO, then input nodes, hidden layer nodes and output nodes are determined, so a load forecasting model based on ALO-ELM combined algorithm is established. The proposed method is illustrated based on the historical load data of a city in China. The results show that the average absolute error of short-term load demand predicted by ALO-ELM model is 1.41, while that predicted by ELM is 4.34, the proposed ALO-ELM algorithm is superior to the ELM and meet the requirements of engineering accuracy, which proves the effectiveness of proposed method.

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Research on Short-Term Load Forecasting Method of Power System Based on Seq2Seq-Attention Model

2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2) ◽

10.1109/ei250167.2020.9346583 ◽

2020 ◽

Author(s):

Wei Hu ◽

Yi Zhang ◽

Qiuting Guo ◽

Xu Huang ◽

Gang Li ◽

...

Keyword(s):

Power System ◽

Load Forecasting ◽

Short Term ◽

Attention Model ◽

Forecasting Method ◽

Short Term Load Forecasting

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An intelligent approach towards very short-term load forecasting

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2021-0012 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Uttamarani Pati ◽

Papia Ray ◽

Arvind R. Singh

Keyword(s):

Genetic Algorithm ◽

Feature Selection ◽

Load Forecasting ◽

Dew Point ◽

Short Term ◽

Spinning Reserve ◽

Feature Selection Technique ◽

Selection Technique ◽

Load Demand ◽

Short Term Load Forecasting

Abstract Very short term load forecasting (VSTLF) plays a pivotal role in helping the utility workers make proper decisions regarding generation scheduling, size of spinning reserve, and maintaining equilibrium between the power generated by the utility to fulfil the load demand. However, the development of an effective VSTLF model is challenging in gathering noisy real-time data and complicates features found in load demand variations from time to time. A hybrid approach for VSTLF using an incomplete fuzzy decision system (IFDS) combined with a genetic algorithm (GA) based feature selection technique for load forecasting in an hour ahead format is proposed in this research work. This proposed work aims to determine the load features and eliminate redundant features to form a less complex forecasting model. The proposed method considers the time of the day, temperature, humidity, and dew point as inputs and generates output as forecasted load. The input data and historical load data are collected from the Northern Regional Load Dispatch Centre (NRLDC) New Delhi for December 2009, January 2010 and February 2010. For validation of proposed method efficacy, it’s performance is further compared with other conventional AI techniques like ANN and ANFIS, which are integrated with genetic algorithm-based feature selection technique to boost their performance. These techniques’ accuracy is tested through their mean absolute percentage error (MAPE) and normalized root mean square error (nRMSE) value. Compared to other conventional AI techniques and other methods provided through previous studies, the proposed method is found to have acceptable accuracy for 1 h ahead of electrical load forecasting.

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Comparison of forecasting methods for power system short-term load forecasting based on neural networks

2016 IEEE International Conference on Information and Automation (ICIA) ◽

10.1109/icinfa.2016.7831806 ◽

2016 ◽

Cited By ~ 6

Author(s):

Linlin Zhuang ◽

Hai Liu ◽

Jimin Zhu ◽

Shulin Wang ◽

Yong Song

Keyword(s):

Neural Networks ◽

Power System ◽

Load Forecasting ◽

Short Term ◽

Forecasting Methods ◽

Short Term Load Forecasting

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A comparative study of short-term load forecasting methods in distribution network

Journal of Science, Quy Nhon University ◽

10.52111/qnjs.2021.15103 ◽

2021 ◽

Vol 15 (1) ◽

pp. 23-35

Author(s):

Tuan Ho Le ◽

◽

Quang Hung Le ◽

Thanh Hoang Phan

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Artificial Neural Network ◽

Arima Model ◽

Distribution Network ◽

Load Forecasting ◽

Short Term ◽

Load Demand ◽

Forecasting Methods ◽

Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

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