Smart grid data analytics for decision support

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

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Privacy-Preserving Market for Smart Grid Data Trading

2020 IEEE 7th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) ◽

10.1109/upcon50219.2020.9376520 ◽

2020 ◽

Author(s):

Ritesh Mohan Acharya ◽

Manickavasagam Parvathy Selvan

Keyword(s):

Smart Grid ◽

Privacy Preserving ◽

Grid Data

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

Energies ◽

10.3390/en12193731 ◽

2019 ◽

Vol 12 (19) ◽

pp. 3731 ◽

Cited By ~ 1

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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