Comparing ELM Against MLP for Electrical Power Prediction in Buildings

Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19

Applied Energy ◽

10.1016/j.apenergy.2021.118458 ◽

2022 ◽

Vol 309 ◽

pp. 118458

Author(s):

Chenxi Hu ◽

Jun Zhang ◽

Hongxia Yuan ◽

Tianlu Gao ◽

Huaiguang Jiang ◽

...

Keyword(s):

Transfer Learning ◽

Electrical Power ◽

Small Sample ◽

Black Swan ◽

Power Prediction ◽

Sample Transfer

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Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

Energies ◽

10.3390/en13030740 ◽

2020 ◽

Vol 13 (3) ◽

pp. 740 ◽

Cited By ~ 1

Author(s):

Jesus L. Lobo ◽

Igor Ballesteros ◽

Izaskun Oregi ◽

Javier Del Ser ◽

Sancho Salcedo-Sanz

Keyword(s):

Power Plants ◽

Electrical Power ◽

Predictive Performance ◽

Power Production ◽

Combined Cycle ◽

Machine Learning Techniques ◽

Power Prediction ◽

New Information ◽

Power And Energy Systems ◽

Power And Energy

The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.

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Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers

Sustainability ◽

10.3390/su13042336 ◽

2021 ◽

Vol 13 (4) ◽

pp. 2336

Author(s):

Hossein Moayedi ◽

Amir Mosavi

Keyword(s):

Power Plants ◽

Water Cycle ◽

Electrical Power ◽

Optimal Solution ◽

Population Based ◽

Influential Factors ◽

Combined Cycle ◽

Power Prediction ◽

Satin Bowerbird ◽

Ant Lion

Predicting the electrical power (PE) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of PE, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the PE with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the PE and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the PE with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented.

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Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines

Energies ◽

10.3390/en14040891 ◽

2021 ◽

Vol 14 (4) ◽

pp. 891

Author(s):

Suo Li ◽

Ling-ling Huang ◽

Yang Liu ◽

Meng-yao Zhang

Keyword(s):

Neural Network ◽

Wind Power ◽

Output Power ◽

Electrical Power ◽

Offshore Wind ◽

Health Conditions ◽

Power Prediction ◽

Short Term ◽

Offshore Wind Power ◽

Wind Power Prediction

More accurate wind power prediction (WPP) is of great significance for the operation of electrical power systems, as offshore wind power penetration increases continuously. As the offshore wind turbines (OWT) are a key system in converting offshore wind power into electrical power, maintaining their condition plays a pivotal role in WPP. However, it is seldom considered in traditional WPP. This paper proposes an ultra-short term offshore WPP methodology based on the condition assessment (CA) of OWTs. Firstly, a modified fuzzy comprehensive evaluation (MFCE) based CA of the OWT is presented with a new defined deterioration of indicators calculated by the relative errors. Long short-term memory (LSTM) neural network is introduced to deal with the complicated interactions between the various monitoring data of an OWT and the dynamic marine environment. Then, with the classifications of the health conditions of the OWT, the historical operation data is classified accordingly. An OWT-condition based WPP with a backpropagation (BP) neural network is developed to deal with the non-linear mapping relations between the numerical weather prediction (NWP) information, health conditions of OWT, and the output power. The results of the case study show the influences of the OWT health conditions to its output power and verifies the effectiveness and higher accuracy of the proposed method.

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