Trend Analysis of Building Power Consumption Based on Prophet Algorithm

Gong, Feixiang; Han, Na; Li, Dezhi; Tian, Shiming

doi:10.1109/aeees48850.2020.9121548

Cited by 11 publications

(3 citation statements)

References 5 publications

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“…Gong et al [12] and Septiani et al [13] deployed Prophet to analyze the power consumption trends of buildings. The comparison of the prediction results of Prophet and ARIMA algorithms shows that Prophet performs better and can consider dates and times.…”

Section: Literature Reviewmentioning

confidence: 99%

Failure Prediction and Intelligent Maintenance of a Transportation Company’s Urban Fleet

Foké¹,

Kenné²,

Diego³

2023

JTTs

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The present work deals with intelligent vehicle fleet maintenance and prediction. We propose an approach based primarily on the history of failures data and on the geographical data system. The objective here is to predict the date of failures for a fleet of vehicles in order to allow the maintenance department to efficiently deploy the proper resources; we further provide specific details regarding the origins of failures, and finally, give recommendations. This study used the Société de transport de Montréal (STM)'s historical bus failure data as well as weather data from Environment Canada. We thank Facebook's Prophet, Simple Feed-forward, and Beats algorithms (Uber), we proposed a set of computer codes that allow us to identify the 20% of buses that are responsible for the 80% of failures by mean of the failure history. Then, we deepened our study on the unreliable equipments identified during the diffusion of our computer code This allowed us to propose probable predictions of the dates of future failures. To ensure the validity of the proposed algorithm, we carried out simulations with more than 250,000 data. The results obtained are similar to the predicted theoretical values.

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Section: Literature Reviewmentioning

confidence: 99%

Failure Prediction and Intelligent Maintenance of a Transportation Company’s Urban Fleet

Foké¹,

Kenné²,

Diego³

2023

JTTs

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“…From the comparison, it was found that the PROPHET model achieved better accuracy. From the outcome, it was also found that the power utilization of shopping malls was most affected by high-temperature weather and holidays to some extent when compared with the utilization of the office buildings (19) .…”

Section: Introductionmentioning

confidence: 99%

Electricity requirement prediction using time series and Facebooks PROPHET

Chadalavada¹,

Raghavendra²,

Rekha³

2020

IJST

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“…Machine Learning techniques and tools [8] can be utilized to handle large data and intelligently foresee the spread of the disease. This paper here considers and discusses the proposed prediction models of COVID-19 spread in India as well as some states/UTs like Maharashtra, Delhi, West Bengal, Tamil Nadu and Odisha using Gated Recurrent Unit (GRU) [9] and Facebook's Prophet [10]. These models will predict the number of confirmed cases in India and the states mentioned one month ahead i.e.…”

Section: Introductionmentioning

confidence: 99%

Forecasting COVID-19 Pandemic in India and its Impact on Planet, People and Profit

Patra

Darshan

Abba

2020

int.jour.sci.res.mana.

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The outbreak of coronavirus disease 2019 (COVID-19) has created a global health crisis that has had a deep impact on humanity. Due to the increasing number of daily cases it is a necessity to develop a prediction method. This paper aims at predicting the number of Coronavirus cases in India ahead of a month and also predicting individually some specific states/UT of India. Gated Recurrent Unit (GRU) and Facebook Prophet are used for prediction. GRU is used to show the accuracy of the neural network model and Facebook Prophet is used to predict the cases. At last the paper will elucidate the indirect effects of lockdown in India. This section will shed light on the indirect effects of the lockdown on people, planet and profit during the lockdown period taking account of the situation in India. This detailed annotation will build a foundation for the people of India to be prepared for upcoming days.

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Trend Analysis of Building Power Consumption Based on Prophet Algorithm

Cited by 11 publications

References 5 publications

Failure Prediction and Intelligent Maintenance of a Transportation Company’s Urban Fleet

Failure Prediction and Intelligent Maintenance of a Transportation Company’s Urban Fleet

Electricity requirement prediction using time series and Facebooks PROPHET

Forecasting COVID-19 Pandemic in India and its Impact on Planet, People and Profit

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