Electricity Price Prediction for Geographically Distributed Data Centers in Multi-Region Electricity Markets

Than, Moh Moh; Thein, Thandar

doi:10.1109/ccoms.2018.8463272

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Previously, machine learning techniques have been used to predict a number of other fields (linear regression technique) [30,31] stock price (deep belief network) [32] price movement (LSTM neural network algorithm) [33], real Estate price [34], amazon spot price (ANFIS model) [35], closing price prediction. (two dimensional principal component analyses) [36], electricity price prediction (neural network model) [37], housing price (neural network) [38]. However, previous studies did not focus on the bio energy; we have predicted the future bio energy production.…”

Section: Introductionmentioning

confidence: 99%

Analysis of bioenergy by using linear regression

et al. 2019

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Prediction of bioenergy production is challenging in the machine learning field. In this article, bioenergy production data from different countries (Russian Fed, Turkey, Armenia, Azerbaijan and total Eurasia) were analyzed to predict future bioenergy production in these countries using the linear regression. Data on output production of bioenergy in megawatt were obtained from the International Renewable Energy Agency, and (MW). We implemented the linear regression method redundant to predict the future production of bioenergy data and attained good accuracy. The aforementioned method is applicable to developing countries and can be used to predict bioenergy production and formulate new policies that are adapted to an increasing population in a particular country. Our results (average accuracy is 49.61%) revealed that bioenergy production levels in developing countries are not sufficient to cater for bioenergy needs of fast growing populations. Thus we recommend that governments should formulate adequate policies aimed at improving bioenergy production.

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