Text|Qianbufan
Editor|Qianbufan
introduce
Cloud computing is a powerful platform that supports large-scale data processing in parallel distributed environments; It consists of a client-server architecture that includes services, protocols, and infrastructure to perform remote tasks more efficiently than the traditional master-slave model.
Figure 1
Cloud architectures are further divided into several types, namely location-based, service-based, and environment-based cloud computing; Accessibility-based cloud computing refers to functional prototypes that involve public, private, hybrid, and community access by clients in remote namespaces.
The architecture of the green cloud involves two types of master-slave communication, namely (i) fog computing and (ii) centralized computing.
The smart grid is part of a green cloud environment where decentralized power distribution units perform functional operations in a self-driving intelligent environment, as shown in Figure 1.
Smart grid architecture
The smart meter records the distribution unit information and bundles it on the node in tabular form, then converts the compiled package into a grid-readable format, i.e., RDF, and the distribution node then processes the RDF dataset through the transformation channel and stores them in the semantic library, as shown in Figure 2.
Figure 2
Smart meter RDF data is stored in the smart grid
Ideally, smart meters should produce information records in a normal grid environment, have a sufficiently long recommended life and optimize the use of energy consumption, in reality, smart meters have environmental problems that lead to abnormally shortened life and abnormal energy consumption.
overview
In this section, we will provide a brief overview of smart grid, smart metering, and semantic mesh technologies.
A. Smart grids
From a future perspective, smart grids are considered to be the "next generation of power supply" and have become the convergence of information technology with power system engineering and communication technologies, and the idea behind smart grids depends on enhancing conventional grids with smart technologies.
Smart technologies include grid digitization with two-way wired and wireless communication channels, i.e. WiMax, Wifi, optical fiber, etc., where distribution units use smart meters to collect record information and store it in storage nodes, where information records are stored in semantic awareness libraries and assist the smart grid in analysis; Smart grids support self-healing capabilities that help handle failures and outages during the recording and storage of smart meter information in the semantic library.
B. Smart meters
Smart meters are used as basic recording devices for distribution units, and the meters use two-way communication between themselves and the smart grid, manage sensor information into built-in RAM, limit up to 1.3 million records, and publish it as a table through distribution nodes.
Smart meters are programmed to work in multiple shifts, i.e. peak times include the maximum resource usage timeline, while the shutdown time includes the normal resource usage timeline, and under normal workloads with an ideal environment, the smart meter has a lifespan of 5 to 7 years.
C. Semantic web technology
The Semantic Web is an extension of the existing World Wide Web where information is described in a meaningful format, and the Semantic Web consists of ontologies, schemas, Internationalized Resource Identifiers (IRIs), and service discovery languages.
Frameworks that support working with datasets include Resource Description Framework (RDF), RDF Schema (RDFS), Simple Knowledge Organization System (SKOS), SPARQL protocol and RDF Query Language (SPARQL), Notation3 (N3), N-Triples, Terse RDF Triple Language (Turtle), Web Ontology Language (OWL), and Rule Interchange Format (RIF).
Recommended method
The proposed approach involves operational steps to increase lifetime and optimize energy consumption, i.e. i. i) smart grid repository, ii) RDF-based personalization graphs, iii) approximate analysis of lifetime and energy consumption tuple datasets, iv) error factors in personalized tuple datasets, v) increasing the lifetime of smart meters, vi) optimizing energy consumption of smart meters, and finally vii) zoning-based allocation of smart meters in smart grids.
A. Smart grid repository
The smart grid repository is configured with the Jena-TDB triplestore function, which contains the record information of the distribution nodes, the RDF tuple, as shown in Figure 3.
Figure 3
Smart grid RDF tuple library
Let Dn be an RDF dataset with triple storage RS=Store{triple(s,p,o)} as the resident entity SGrepository of the smart grid repository, this concept can be expressed as (x rdf:type ex:property) and the triplet can be defined as:
where I is IRI, N is the number of nodes, and L is the number of words.
Let G be the default graph n with a finite source node set has π as a suitable attribute in the dataset, so that graph G can be represented as a product set:
Figure 4: Basic union diagram
B. RDF-based personalized graph
The smart grid consists of a huge semantic library SGrepository, which stores tuple datasets of all distribution nodes, and by default, RDF graph databases provide a query technique that provides custom extractions of tuple extraction.
Smart grid processing streams RDF storage to SGrepository, so query-based RDF graph extraction technology is less feasible, and personalized RDF graph provides a custom method for extracting datasets from large-scale streaming storage for SGrepository's smart grid.
Algorithm-1 generates personalized graphs to extract subplots d with depth from the dataset Di At the end of instances iwhereiεI (set of instances), cross-label Lm to add all relevant subgraphs and generate personalization plot G.
Let G extract the personalized graph SGrepository of the custom dataset from the library and propose a method to maintain a continuous tuple stream using the same label placement iteration, where a single label appears between instances Lmo→b. Personalized graph G between two instances can be calculated as:
Predicate Path Set (PPS) collects predicates with a Scani array and combines two or more PPS sets by formulating a two-dimensional array IDXJ and RFLT operators, as shown in Figure 5, as detailed as:
The scan search for the subgraph is shown in Figure 6, and the GLifepan of the graph search G (Search) graph yields:
Figure 5 shows the chart personalization RDF filter G
Figure 6 Personalizing RDF Filters to Merge Subgraphs
To obtain the personalized tuple dataset GLifespan of the graph, we use eq (4) and eq (8) as:
GLifepan is a custom tuple dataset that personalizes charts and obtains smart meter life values; Similarly, we get a personalized diagram GEnergy as:
C. Approximate analysis of lifetime and energy consumption tuple datasets
By default, smart meters consume life through standard procedures because:
1) Smart meter load shifting
The smart grid starts the meter in two shifts, i.e. (i) peak load and (ii) offload, and the tuples generated at peak load are stored in the SGrepository and represented as:
D. Error factors in personalized tuple datasets
The extraction of personalized tuple datasets can generate false anomalies due to unidentified edges, unknown vertices, and array out-of-index problems in peaks and blocks, and error percentiles can be obtained in the following ways:
E. Extension of the life of smart meters
The proposed method improves the useful life of smart meters by calculating the remaining life SMRL as:
GLifespan, calculated from the tuples of the peak and off block datasets, influencing factors SMRL include life expectancy LEf, genetic GEf, environmental factor EFf, change over time CTf and finite lifetime LLf. The exchange factor ΔF can be expressed as:
The maximum utilization ΔTLifepan was found using the Hungarian algorithm, as shown in Figure 7.
Figure 7 Minimum lifetime using the Hungarian algorithm
F. Optimization of energy consumption of smart meters
The proposed method reduces the energy consumption of smart meters by calculating the residual energy cycle meter RE as:
The maximum utilization ΔTEnergy was found using the Hungarian algorithm, as shown in Figure 8.
Figure 8 Maximum energy consumption using the Hungarian algorithm
G. Zoning distribution of smart meters in the smart grid
If the smart meter coefficient consumption is between 0 and 30%, it is declared "FULL" and put into slot "A", which uses the full capacity of the smart meter for peak and shutdown blocks.
If the smart meter coefficient consumption is between 30% and 50%, it is declared "not full" and put into slot "B", which uses the smart meter capacity 1/2 to enter the peak and shutdown block.
Figure 9 The default smart meter is placed in the smart grid
If the smart meter coefficient consumption is between 50% and 70%, it is declared "HALF" and put into slot "C", which uses the smart meter capacity 1/3 into the peak and off block.
If the smart meter coefficient consumption is between 70% and 95%, declare "NOT HALF" and put it into slot "D", which uses smart meter capacity 1/6 to divide into peak and off blocks, as shown in Figure 10.
Figure 10 Zone-based smart meters are placed in the smart grid
Performance evaluation
The proposed method is evaluated by smart meter tuple dataset stored in the semantic triplet Jena-TDB. The evaluation includes, (i) generating a personalization map, (ii) tuple datasets of peak loads and offloading blocks, (iii) error rate percentiles in the personalization graph, (iv) threshold extraction of factors affecting lifetime and energy consumption, and finally (v) placing smart meters in their respective slots for optimal utilization of the smart grid.
A. Test bench
The test bench includes Jena-TDB version 3.0.1 repository configuration configured on Intel Core(TM) i5–3470 CPU @ 3.20 GHz with 6GM RAM, Windows 8.1 64-bit operating system with x64-based processor, over 250GB WD hard drive.
B. Smart meter datasets
We use a comprehensive workload for smart meters with distributed node tuple datasets, Jena-TDB is configured for a 300 GB tuple dataset workload, and the workload is configured with (i) peak load and (ii) offload.
C. Personalized graphics generation
The personalization graph generation process builds a set of custom queries that take tuples from the Jena-TDB repository, and there are two types of custom queries: (i) static queries and (ii) dynamic queries.
Static queries take the power of the personalize graph to get tuples related to lifetime and energy consumption, dynamic queries build an array to stack continuous tuple streams, and build an array for the inputs of static queries, as shown in Figure 12.
Figure 11 Personalized graph generation in the peak-block SG repository
Figure 12 Personalization Graph Generation in the Out-of-Block SG Repository
D. Peak-to-Off Smart meters in a block tuple dataset
Personalization maps extract unstructured datasets containing unordered tuple sets of smart meters, and identifying the number of smart meters used to generate semantic tuples into such a large data set adds complexity, as shown in Figure 13.
Figure 13 Smart meter node in a 20GB repository
Figure 14 Smart meter node in a 50GB repository
Figure 15 Smart meter node in a 100 GB repository
Figure 16 Smart meter node in a 150 GB repository
E. Error rate percentiles in the Personalize Graph
Tuple extraction in the Personalize Graph causes error exceptions due to unrecognized edges, unknown vertices, and array out of index issues, which occur due to the incorrect insertion of tuple values, i.e. value#, and returning unrecognized format tuples.
Figure 17 Error Rate in Personalization Chart
F. Threshold extraction of influencing factors of life and energy consumption
The lifetime peak and off tuple datasets evaluated by the discussed filter found that the peak dataset contained 30 smart meters consuming an average of 11% LE, 12% GE, 14% EF, 19% CT, and 3% LL, as shown in Figure 18.
The peak and close tuple datasets were found by Hungarian algorithm filtering, and the shutdown dataset contained 30 smart meters, consuming an average of 9% LE, 11.7% GE, 13.8% EF, 18.6% CT and 2.9% LL as shown in Figure 19.
Finding the energy peak tuple dataset through the Hungarian filter, it was found that the off dataset contained 100 smart meters consuming an average of 2% RU, 12% MC, 14% VD, 19% O, and 64% AV, as shown in Figure 20.
The off tuple dataset of energy evaluated by the same Hungarian filter found that the off dataset contained 100 smart meters consuming an average of 1.9% RU, 11.8% MC, 13.9% VD, 18.8% O and 60% AV as shown in Figure 21.
Figure 18 Factors affecting peak block lifetime
Figure 19 Factors affecting out-of-block lifespan
Figure 20 Factors affecting energy use during peak hours
Figure 21 Factors influencing energy use outside the block
G. Smart meter-based placement based on zones
The slot configuration of a smart meter consists of a controller that manages the logging requests of the distribution nodes and instructs the smart meter to perform actions within a defined time frame, and it also controls the configuration script that generates the number of records per cycle of the smart meter.
Figure 22 Smart meters are assigned to partition-based slots
Conclusions and future work
This paper proposes a knowledge-based RDF personalization graph generation technique that extracts custom tuple datasets from the continuous streaming semantic library of green cloud-based smart grids.
The proposed method discusses peak and breakblock personalization diagrams as well as possible error anomalies and extraction of threshold factors affecting smart meter life and energy consumption.
The proposed method provides an effective filter that can identify disordered elements within the threshold factors of lifetime and energy expenditure.
The personalized graph method has improved the overall life of smart meters in green cloud-based smart grids by 72% and reduced energy consumption by 21%, and the effective data retrieval and compression technology of smart meters in green cloud-based smart grids will be studied in the future.
bibliography
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