Long-term forecast of energy commodities price using machine learning

Herrera, Gabriel Paes; Constantino, Michel; Tabak, Benjamin Miranda; Pistori, Hemerson; Su, Jen‐Je; Naranpanawa, Athula

doi:10.1016/j.energy.2019.04.077

Cited by 95 publications

(46 citation statements)

References 36 publications

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“…Lohrmann and Luukka (2019) interpreted the classification of the S&P500 open-to-close returns as a four-class problem and compared four trading strategies based on a random forest classifier to a buyand-hold strategy. Herrera et al (2019) compared the long horizon forecast performance of traditional econometric models with machine learning methods (Neural Networks and Random Forests), for the main energy commodities in the world, using monthly prices provided by the International Monetary Fund (IMF). Ciner (2019) showed that when the random forest method, which accounts for both linear and nonlinear dynamics, was used for regression, then industry returns indeed contain significant out of sample forecasting power for the market index return.…”

Section: Random Forest Algorithmmentioning

confidence: 99%

Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms

Zang

et al. 2021

Journal of Global Information Management

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Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies.

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Section: Random Forest Algorithmmentioning

confidence: 99%

Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms

Zang

et al. 2021

Journal of Global Information Management

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“…We performed and evaluated four different methods, a hybridization of traditional econometric models, artificial neural networks, random forests, and the no-change method, as described in [1]. The last one implies that changes in an observation value are unpredictable, so the best forecast is simply the current observation value.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Data on forecasting energy prices using machine learning

et al. 2019

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This article contains the data related to the research article “Long-term forecast of energy commodities price using machine learning” (Herrera et al., 2019). The datasets contain monthly prices of six main energy commodities covering a large period of nearly four decades. Four methods are applied, i.e. a hybridization of traditional econometric models, artificial neural networks, random forests, and the no-change method. Data is divided into 80-20% ratio for training and test respectively and RMSE, MAPE, and M-DM test used for performance evaluation. Other methods can be applied to the dataset and used as a benchmark.

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“…12 The multi-boundary score is used for predicting solar energy generation 13 and solar irradiance prediction using deep neural networks. 14 Wind speed forecasting wavelet transform and support vector machines, 15 Artificial neural networks (ANN), 16 SVM, 17 random forest, 18 K-nearest neighbor (KNN), Support vector regression (SVR) 19,20 models predict wind and solar power generation using weather information. Blending approaches are used to improve the regional forecast with support vector machines to forecast short-term solar radiation system.…”

Section: Renewable Energy Forecastingmentioning

confidence: 99%

A holistic review on energy forecasting using big data and deep learning models

Devaraj¹,

Elavarasan

Shafiullah

et al. 2021

Int J Energy Res

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With the growth of forecasting models, energy forecasting is used for better planning, operation, and management in the electric grid. It is important to improve the accuracy of forecasting for a faster decision-making process. Big data can handle large scale of datasets and extract the patterns fed to the deep learning models that improve the accuracy than the traditional models and hence, recently started its application in energy forecasting. In this study, an in-depth insight is initially derived by investigating artificial intelligence (AI) and machine learning (ML) techniques with their strengths and weaknesses, enhancing the consistency of renewable energy integration and modernizing the overall grid. However, Deep learning (DL) algorithms have the capability to handle big data by capturing the inherent non-linear features through automatic feature extraction methods. Hence, an extensive and exhaustive review of generative, hybrid, and discriminative DL models is being examined for short-term, medium-term, and long-term forecasting of renewable energy, energy consumption, demand, and supply etc. This study also explores the different data decomposition strategies used to build forecasting models. The recent success of DL is being investigated, and the insights of paradoxes in parameter optimization during the training of the model are identified. The impact of weather prediction in the wind and solar energy forecasting is examined in detail. From the existing literatures, it has seen that the average mean absolute percentage error (MAPE) value of solar and wind energy forecasting is 10.29% and 6.7% respectively. Current technology barriers involved in implementing these models for energy forecasting and the recommendations to overcome the existing system barriers are identified. An indepth analysis, discussions of the results, and the scope for improvement are provided in this study including the potential directions for future research in the energy forecasting.

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Long-term forecast of energy commodities price using machine learning

Cited by 95 publications

References 36 publications

Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms

Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms

Data on forecasting energy prices using machine learning

A holistic review on energy forecasting using big data and deep learning models

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