Smart grid data analytics framework for increasing energy savings in residential buildings

2016 ◽  
Vol 72 ◽  
pp. 247-257 ◽  
Author(s):  
Jui-Sheng Chou ◽  
Ngoc-Tri Ngo
Keyword(s):  
Smart Grid ◽  
Data Analytics ◽  
Energy Savings ◽  
Residential Buildings ◽  
Grid Data

Smart grid data analytics for digital protective relay event recordings

2013 ◽  
Vol 17 (3) ◽  
pp. 591-600 ◽  
Author(s):  
Tomo Popovic ◽  
Mladen Kezunovic ◽  
Bozo Krstajic
Keyword(s):  
Smart Grid ◽  
Data Analytics ◽  
Grid Data ◽  
Protective Relay

Privacy-Preserving Homomorphic Masking for Smart Grid Data Analytics in the Cloud

2020 ◽  
pp. 265-292
Author(s):  
Pirathayini Srikantha ◽  
Anna Shi ◽  
Bokun Zhang ◽  
Deepa Kundur
Keyword(s):  
Smart Grid ◽  
Data Analytics ◽  
Privacy Preserving ◽  
Grid Data

Energy Prediction Using Data Analytics in Smart Grid

2018 ◽  
Vol 06 (06) ◽  
pp. 110-115
Author(s):  
Panchami Anil ◽  
Anas P V ◽  
Naseef Kuruvakkottil ◽  
Anusha K V ◽  
Balagopal N
Keyword(s):  
Smart Grid ◽  
Data Analytics ◽  
Energy Prediction ◽  
Using Data

scholarly journals Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3876
Author(s):  
Sameh Monna ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
Aiman Albatayneh ◽  
Patrick Dutournie ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Energy Savings ◽  
Residential Buildings ◽  
Residential Building ◽  
Simulation Models ◽  
Water Heating ◽  
Heating And Cooling ◽  
Space Cooling ◽  
Cooling Loads

Since buildings are one of the major contributors to global warming, efforts should be intensified to make them more energy-efficient, particularly existing buildings. This research intends to analyze the energy savings from a suggested retrofitting program using energy simulation for typical existing residential buildings. For the assessment of the energy retrofitting program using computer simulation, the most commonly utilized residential building types were selected. The energy consumption of those selected residential buildings was assessed, and a baseline for evaluating energy retrofitting was established. Three levels of retrofitting programs were implemented. These levels were ordered by cost, with the first level being the least costly and the third level is the most expensive. The simulation models were created for two different types of buildings in three different climatic zones in Palestine. The findings suggest that water heating, space heating, space cooling, and electric lighting are the highest energy consumers in ordinary houses. Level one measures resulted in a 19–24 percent decrease in energy consumption due to reduced heating and cooling loads. The use of a combination of levels one and two resulted in a decrease of energy consumption for heating, cooling, and lighting by 50–57%. The use of the three levels resulted in a decrease of 71–80% in total energy usage for heating, cooling, lighting, water heating, and air conditioning.

scholarly journals The IEC 61850 Sampled Measured Values Protocol: Analysis, Threat Identification, and Feasibility of Using NN Forecasters to Detect Spoofed Packets

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3731 ◽  
Author(s):  
Mohamad El Hariri ◽  
Eric Harmon ◽  
Tarek Youssef ◽  
Mahmoud Saleh ◽  
Hany Habib ◽  
...  
Keyword(s):  
Smart Grid ◽  
Protocol Analysis ◽  
Data Detection ◽  
Detection Accuracy ◽  
Wide Spread ◽  
Grid Data ◽  
Iec 61850 ◽  
Forecasting Error ◽  
Heterogeneous Nature ◽  
Statistical Indicators

The operation of the smart grid is anticipated to rely profoundly on distributed microprocessor-based control. Therefore, interoperability standards are needed to address the heterogeneous nature of the smart grid data. Since the IEC 61850 emerged as a wide-spread interoperability standard widely accepted by the industry, the Sampled Measured Values method has been used to communicate digitized voltage and current measurements. Realizing that current and voltage measurements (i.e., feedback measurements) are necessary for reliable and secure noperation of the power grid, firstly, this manuscript provides a detailed analysis of the Sampled Measured Values protocol emphasizing its advantages, then, it identifies vulnerabilities in this protocol and explains the cyber threats associated to these vulnerabilities. Secondly, current efforts to mitigate these vulnerabilities are outlined and the feasibility of using neural network forecasters to detect spoofed sampled values is investigated. It was shown that although such forecasters have high spoofed data detection accuracy, they are prone to the accumulation of forecasting error. Accordingly, this paper also proposes an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed methods is experimentally verified in a laboratory-scale smart grid testbed.

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