Implementing “R” Programming for Time Series Analysis and Forecasting of Electricity Demand for Texas, USA

Ngabesong, Robert; McLauchlan, Lifford

doi:10.1109/greentech.2019.8767131

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…A few reviews validated that machine learning and modelling have been the best technique to predict stock prices. Ngabesong and McLauchlan (2019) "Implementing 'R' Programming for Time Series Analysis and Forecasting of Electricity Demand for Texas, USA" forecasted electric supply for Texas on the basis of historical data of one year on a one-point data from September 2016 to August 2017. The Auto Regressive Integrated Moving Average (ARIMA) model was used to estimate future predictions of electricity demand for Texas.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Optimized Model on Money Markets Instruments With Capital Market and Bank Rates Ratio

Hungund

Rastogi

2023

International Journal of Data Analytics

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The money market and the capital market of the Indian financial markets have a symbiotic relationship in the development of the Indian economy. The nature and the characteristics of the markets differ to a large extent as the money market ensures liquidity in the system through the monetary policy by the regulators; capital markets propel and act as the engine driver for the economy in the long term. Therefore, the final throughput of the economy is the aggregation of the output of both the markets. Does that imply that the development of both markets is parallel in nature or is any one superior to the other or are they competitors? To understand the influence of one over the other the research was undertaken through a correlation matrix and time series model. A predictive model was further constructed for predicting the volume of money market instrument on the basis of fourteen days historical.

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Section: Literature Reviewmentioning

confidence: 99%

Predictive Optimized Model on Money Markets Instruments With Capital Market and Bank Rates Ratio

Hungund

Rastogi

2023

International Journal of Data Analytics

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“…With advancement of information and communication technology as well as machine learning (ML) and deep learning (DL) techniques, data driven models for electricity demand and price forecasting is becoming a hot research topic [15][16][17][18][19]. These techniques would be very beneficial for decision making by system operators, system owners and policy makers.…”

Section: Literature Reviewmentioning

confidence: 99%

Web-Based Electricity Cost Modeling for Bangladesh Power Sector to Improve Capacity and Transparency

Alam

Zaman²,

Rupom

et al. 2020

Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1

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A study project undertaken for developing a web-based electricity cost model (WECM) is presented in this paper. The project targets to complete by five years that includes development of a prototype application, followed by a pilot phase with various cost analysis of real-life data and finally a nation-wide implementation of the model in the power sector of Bangladesh. The overall objectives of this study project are: (i) design and develop a well-structured, scalable database and a web application (ii) capacity building in Web and mobile app development and (iii) integrate machine learning based analytics using real-life data. This paper discusses the detailed methodology used for the web application development and capacity building. In addition, the first prototype application developed, and future scope are also presented. Finally, the expected outcome of this ongoing study and benefit of WECM are discussed. The proposed model for migrating the operation of one of the vital sectors i.e. the energy sector in Bangladesh to cloud is a bold step towards "Digital Bangladesh". Upon successful completion of this study project, it is expected that about 90% of the energy sector's data can be automatically captured and reported for management and operation using WECM.

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“…Time series forecasting can provide information about the future electricity requirement for the utility operators. These predictions can assist as knowledge to utility operators to plan and determine when the highest shaving is needed (10) .…”

Section: Introductionmentioning

confidence: 99%

Electricity requirement prediction using time series and Facebooks PROPHET

Chadalavada¹,

Raghavendra²,

Rekha³

2020

IJST

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Implementing “R” Programming for Time Series Analysis and Forecasting of Electricity Demand for Texas, USA

Cited by 8 publications

References 9 publications

Predictive Optimized Model on Money Markets Instruments With Capital Market and Bank Rates Ratio

Predictive Optimized Model on Money Markets Instruments With Capital Market and Bank Rates Ratio

Web-Based Electricity Cost Modeling for Bangladesh Power Sector to Improve Capacity and Transparency

Electricity requirement prediction using time series and Facebooks PROPHET

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