With population increases and a vital need for energy, energy systems play an important and decisive role in all of the sectors of society. To accelerate the process and improve the methods of responding to this increase in energy demand, the use of models and algorithms based on artificial intelligence has become common and mandatory. In the present study, a comprehensive and detailed study has been conducted on the methods and applications of Machine Learning (ML) and Deep Learning (DL), which are the newest and most practical models based on Artificial Intelligence (AI) for use in energy systems. It should be noted that due to the development of DL algorithms, which are usually more accurate and less error, the use of these algorithms increases the ability of the model to solve complex problems in this field. In this article, we have tried to examine DL algorithms that are very powerful in problem solving but have received less attention in other studies, such as RNN, ANFIS, RBN, DBN, WNN, and so on. This research uses knowledge discovery in research databases to understand ML and DL applications in energy systems’ current status and future. Subsequently, the critical areas and research gaps are identified. In addition, this study covers the most common and efficient applications used in this field; optimization, forecasting, fault detection, and other applications of energy systems are investigated. Attempts have also been made to cover most of the algorithms and their evaluation metrics, including not only algorithms that are more important, but also newer ones that have received less attention.

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Machine Learning and Deep Learning in Energy Systems: A Review

Forecasting techniques are used in the various problem domains such as- sales, banking, healthcare, stock market, etc. The time-series dataset has time-related information that is useful for prediction and statistical analysis. The supermarket sales prediction helps improve sales in a business environment. The technique helps in decision making in a problem domain. Many tools are available for forecasting such as the regression model, Logistic exponential model. The Facebook (FB) Prophet is the latest tool that has shown an improved performance in terms of accuracy of prediction. This research work has proposed a FB Prophet tool for the sales prediction of the supermarket data. The proposed research work has examined few forecasting models such as- The additive model, the Autoregressive integrated moving average (ARIMA) model, FB Prophet model. From the propsoed research work, it is concluded that, FB Prophet is a better prediction model in terms of low error, better prediction, and better fitting.

Time Series Forecasting Model for Supermarket Sales using FB-Prophet

Ham petrol ve petrol urunleri, endustriyel uretimin onemli girdileri arasinda oldugu kadar lojistik ve tasimacilikta da kritik bir rol oynamaktadir. Dolayisiyla, petrol fiyatlarindaki ani artislar ve dususler kuresel ekonomilerde ve dahasi ekonomiler uzerinde dogrudan veya dolayli bir etkisi vardir. Ayrica, gelismekte olan ekonomilerdeki krizler, buyuk ekonomiler arasindaki ticaret anlasmazliklari ve petrol fiyatinin dinamik dogasi, petrol arz ve talebi uzerinde etkisi olmaktadir ve petrol fiyatinda zaman zaman oynaklik cok sert olmaktadir. Petrol fiyatlarindaki bu belirsizlikler hem tuketicilere hem de ureticilere agir potansiyel kayiplar yaratabilmektedir. Bu hizli degiskenlik ve dalgalanma nedeniyle petrol fiyatlarinin tahmin edilmesi kuresel oneme sahiptir. Bu calismada, Brent Petrol fiyatlarinin gelecekteki trendini tahmin edilebilmek icin gecmis degerleri girdi alan Uzun Kisa Sureli Bellek (LSTM) ve Facebook Prophet (FBPr) yontemleri kullanilmistir. Iki modelin petrol fiyatlari icin Haziran 1988 ile Haziran 2020 arasinda haftalik 32 yillik veri seti kullanilarak karsilastirilmis ve en uygun model belirlenmistir. Veri seti egitim ve test setleri olmak uzere iki gruba ayrilmistir; egitim seti icin ilk yirmi bes yil secilirken ve son yedi yil ise tahmin dogrulugunu onaylamak icin kullanilmistir. LSTM ve FBPr modelleri icin katsayi tayini (R2) egitim asamasinda 0.92, 0.89 ve test asamasinda 0.89, 0.62 bulunmustur. Elde edilen sonuclar incelendiginde, LSTM modelinin petrol fiyatlarindaki trendi tahmin etmek icin daha iyi sonuc verdigi gorulmustur.

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The Prediction of Brent Crude Oil Trend Using LSTM and Facebook Prophet

With the advent of the first pandemic wave of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the question arises as to whether the spread of the virus will be controlled by the application of preventive measures or will follow a different course, regardless of the pattern of spread already recorded. These conditions caused by the unprecedented pandemic have highlighted the importance of reliable data from official sources, their complete recording and analysis, and accurate investigation of epidemiological indicators in almost real time. There is an ongoing research demand for reliable and effective modeling of the disease but also the formulation of substantiated views to make optimal decisions for the design of preventive or repressive measures by those responsible for the implementation of policy in favor of the protection of public health. The main objective of the study is to present an innovative data-analysis system of COVID-19 disease progression in Greece and her border countries by real-time statistics about the epidemiological indicators. This system utilizes visualized data produced by an automated information system developed during the study, which is based on the analysis of large pandemic-related datasets, making extensive use of advanced machine learning methods. Finally, the aim is to support with up-to-date technological means optimal decisions in almost real time as well as the development of medium-term forecast of disease progression, thus assisting the competent bodies in taking appropriate measures for the effective management of the available health resources.

Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis

Since December 2019, the novel coronavirus(COVID-19) has caused over 700,000 deaths with more than 10 million people being infected. Bangladesh, the most densely populated country in the world, is now under community trans-mission of the COVID-19 outbreak. This has created huge health, social, and economic burdens. Till the 10th of February 2020, Bangladesh has reported over 500,000 infected cases and 8000 deaths. To prevent further detriment in our scenario, predicting future consequences are very important. Studies have shown that machine learning(ML) models work extremely well in providing precise information regarding COVID-19 to the authorities thus enabling them to make decisions accordingly. However, to the best of our knowledge, no ML models have been applied that can help in determining the pandemic circumstance for Bangladesh demographics. In this study, we explore different machine learning algorithms that can provide more accurate estimations for predicting future cases which includes infections and deaths due to COVID-19 for Bangladesh. Based on this the government and policymakers can make a decision about the lockdown, resource mobilization, etc. Our study shows that in predicting the pandemic situations, amidst many predicting models the Facebook Prophet Model provided the best accuracy. We believe that using this information the authorities can take decisions that will lead to the saving of countless lives of the people. Additionally, this will also help to reduce the immeasurable economic burden our country is facing due to the present status quo. Furthermore, this study will help analysts to construct predicting models for future explorations.

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Predicting COVID-19 infections and deaths in Bangladesh using Machine Learning Algorithms

This paper presents a framework capable of accurately forecasting future sales in the retail industry and classifying the product portfolio according to the expected level of forecasting reliability. The proposed framework, that would be of great use for any company operating in the retail industry, is based on Facebook's Prophet algorithm and backtesting strategy. Real-world sales forecasting benchmark data obtained experimentally in a production environment in one of the biggest retail companies in Bosnia and Herzegovina is used to evaluate the framework and demonstrate its capabilities in a real-world use case scenario.

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Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

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By successfully solving the problem of forecasting, the processes in the work of various companies are optimized and savings are achieved. In this process, the analysis of time series data is of particular importance. Since the creation of Facebook's Prophet, and Amazon's DeepAR+ and CNN-QR forecasting models, algorithms have attracted a great deal of attention. The paper presents the application and comparison of the above algorithms for sales forecasting in distribution companies. A detailed comparison of the performance of algorithms over real data with different lengths of sales history was made. The results show that Prophet gives better results for items with a longer history and frequent sales, while Amazon's algorithms show superiority for items without a long history and items that are rarely sold.

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Comparison Analysis of Facebook's Prophet, Amazon's DeepAR+ and CNN-QR Algorithms for Successful Real-World Sales Forecasting

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Kemal Korjenic

Papers

Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

Comparison Analysis of Facebook's Prophet, Amazon's DeepAR+ and CNN-QR Algorithms for Successful Real-World Sales Forecasting

Comparison Analysis of Facebook’s Prophet, Amazon’s DeepAR+ and CNN-QR Algorithms for Successful Real-World Sales Forecasting