Cloud-Based Software Platform for Big Data Analytics in Smart Grids

Simmhan, Yogesh; Aman, Saima; Kumbhare, Alok; Liu, Rongyang; Stevens, S. E.; Zhou, Qunzhi; Prasanna, Viktor K.

doi:10.1109/mcse.2013.39

Cited by 277 publications

(164 citation statements)

References 6 publications

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“…A profile identification, short-term load forecasting, and customer segmentation methodology was proposed in [15] to compute the daily profiles from estimated models. Another cloud-based Dynamic Demand Response (D 2 R) platform was introduced in [3] to perform curtailment strategy selection, intelligent demand-side management and forecasting, visualize LP patterns, and to relieve peak load. In [16], Fourier transforms and Gaussian time series techniques were investigated to forecast domestic electricity system demand by analyzing LPs.…”

Section: Related Workmentioning

confidence: 99%

“…The analysis of the high velocity, volume and variance of collected AMI data has allowed accurate aggregations for seasonal / long term usage trends, and provided a pattern of the total demands with many associated innovative analytical outcomes, including load profiling, peaks identification, abnormal usages, demand forecasting, electrical thefts, and intelligent pricing tariffs [3][4]. Thus, it allows operators to optimize the energy supply.…”

Section: Introductionmentioning

confidence: 99%

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A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Moussa

2016

MATEC Web Conf.

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Abstract. With the rapidly increase of electricity demand around the world due to industrialization and urbanization, this turns the availability of precise knowledge about the consumption patterns of consumers to a valuable asset for electricity providers, given the current competitive electricity market. This would allow them to provide satisfactory services in time of load peaks and to control fraud and abuse cases. Despite of this crucial necessity, this is currently very hard to achieve in many developing countries since smart meters or advanced metering infrastructures (AMIs) are not yet settled there to monitor and report energy usages. Whereas the communication and information technologies have widely emerged in such nations, allowing the enormous spread of smart devices among population. In this paper, we present mobile-based BLPDA, a novel platform for big data analytics of consumers' load profiles (LPs) in the absence of AMIs' establishment. The proposed platform utilizes mobile computing in order to collect the consumptions of consumers, build their LPs, and analyze the aggregated usages data. Thus, allowing electricity providers to have better vision for an enhanced decision making process. The experimental results emphasize the effectiveness of our platform as an adequate alternative for AMIs in developing countries with minimal cost.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Moussa

2016

MATEC Web Conf.

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“…Load forecasting is not only based on streaming AMI data, but other data such as consumer profile, weather forecast, consumer locality and day of prediction say weekend or weekday or festivals/special days etc.In [1], demand forecasting is done using ARIMA (Auto Regressive Moving Average) and regression tree.Advantages of both the methods are discussed. ARIMA is good at following trends and the Regression tree model has low error rate.…”

Section: B Forecastingmentioning

confidence: 99%

“…[1] discusses the advantages of using private and public clouds. Public clouds are cost efficient whereas private clouds offer more security over sensitive data like customer usage patterns.…”

Section: Cloudmentioning

confidence: 99%

“…Incorporating this feature in smart grids require precise demand response programs. [1] proposes the Dynamic Demand Response Program to help in intelligent decision making via demand forecasting and curtailment forecasting models.These models accurately predict the near real time areas of peak and mismatch and also identify the buildings or customers for curtailment.…”

Section: Demand Responsementioning

confidence: 99%

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A Survey on Realtime Analytics Framework for Smart Grid Energy Management

K.Sornalakshmi¹

2015

IJIRSET

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Smart grids are modernized electricity grids with information technology support. Smart Grids are the most promising development in the energy and utilities market.Smart grids are being installed in many countries and it is expected to have multi-fold benefits in efficient energy management. The Smart Grids receive real time meter data with high velocity and volume. In such scenario, near real time efficient analytics of streaming smart meter data and quick decision making is significant. In this paper, we survey the existing methodologies and means for real time energy data management in smart grids.

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A survey on cloud computing in energy management of the smart grids

Allahvirdizadeh

Moghaddam

Shayanfar

2019

Int Trans Electr Energ Syst

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Summary Smart grid mitigates the environmental concerns associated with the global warming and climate shifting and reduces the dependence of the power generation on conventional fossil fuels. This is done by energy efficiency improvement and renewable energy sources exploitation at large scales in the form of centralized systems as well as small‐scale systems in the form of microgrids and nanogrids. However, smart grid technology brings new challenges to the big data management of the new power system construction. Additionally, due to the large‐scale intermittent distributed renewable resources contribution and stochastic electric vehicles integration, the current energy management systems must undergo some improvements to handle the smart grid requirements. Cloud computing, as an intermittent based on‐demand computing model, serves an emerging solution to the aforementioned challenges. As a result, this paper presents a survey study on the smart grid and state of art energy management methods and the necessitation of the cloud computing incorporation in smart grid energy management. Then, the application of the cloud computing in energy management, demand side management programs, building energy management systems, energy hubs, and power dispatching systems have been discussed and associated models are addressed.

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Cloud-Based Software Platform for Big Data Analytics in Smart Grids

Cited by 277 publications

References 6 publications

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

A Survey on Realtime Analytics Framework for Smart Grid Energy Management

A survey on cloud computing in energy management of the smart grids

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