Journal ArticleDOI
Carbon trading volume and price forecasting in China using multiple machine learning models
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TLDR
Six machine learning models are used to predict the daily carbon price and trading volume of eight carbon markets in China, including Beijing, Shenzhen, Guangdong, Hubei, Shanghai, Fujian, Tianjin, Chongqing, and an advanced data denoising method is used in the models to smooth the raw data.About:
This article is published in Journal of Cleaner Production.The article was published on 2020-03-10. It has received 140 citations till now. The article focuses on the topics: Carbon price & Order (exchange).read more
Citations
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Hybrid decision tree-based machine learning models for short-term water quality prediction.
Hongfang Lu,Hongfang Lu,Xin Ma +2 more
TL;DR: Two novel hybrid decision tree-based machine learning models are proposed to obtain more accurate short-term water quality prediction results and shows that the prediction stability of CEEMDAN-RF and CEEMdAN-XGBoost is higher than other benchmark models.
Journal ArticleDOI
Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks
TL;DR: Multiple comparative experiments show that the proposed CEEMDAN–CNN–LSTM model can accurately forecast the solar irradiance and outperform a large number of alternative methods.
Journal ArticleDOI
Leakage detection techniques for oil and gas pipelines: State-of-the-art
TL;DR: The existing detection methods that can be used in oil and gas pipelines are introduced and their advantages, limitations, applicable occasions, and performance are analyzed so as to provide the reference for the selection of oil andGas pipeline detection technology in engineering.
Journal ArticleDOI
A hybrid model for carbon price forecasting using GARCH and long short-term memory network
TL;DR: Combining econometric and artificial intelligence methods, the proposed model has an excellent performance on the current carbon price, with smaller errors than single econometrics or AI models or decomposition-ensemble models with linear simple superposition approaches.
Journal ArticleDOI
Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower
TL;DR: A novel hybrid model is proposed for predicting short-term building energy consumption using complete ensemble empirical mode decomposition with adaptive noise, and the buildingEnergy consumption is predicted by the traditional extreme gradient boosting.
References
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TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
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XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.
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A tutorial on support vector regression
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