Internet of Things, Challenges for Demand Side Management

Oprea, Simona-Vasilica; Tudorica, Bogdan George; Belciu, Anda; Botha, Iuliana

doi:10.12948/issn14531305/21.4.2017.05

Cited by 7 publications

(10 citation statements)

References 7 publications

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“…Within a group of computers on a connected network, there are collections of webservers, and we have the ability to access files of information on wide varieties of cloud systems. Nowadays mobile and IoT (Internet of Things) users need not know how their jobs are actually executed in distributed environments and they need not take into account their cloud hardware platforms [20].…”

Section: Resultsmentioning

confidence: 99%

Apps-based Machine Translation on Smart Media Devices - A Review

Gunarto

2019

Indonesian J. Comput. Cybern. Syst.

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Machine Translation Systems are part of Natural Language Processing (NLP) that makes communication possible among people using their own native language through computer and smart media devices. This paper describes recent progress in language dictionaries and machine translation commonly used for communications and social interaction among people or Internet users worldwide who speak different languages. Problems of accuracy and quality related to computer translation systems encountered in web & Apps-based translation are described and discussed. Possible programming solutions to the problems are also put forward to create software tools that are able to analyze and synthesize language intelligently based on semantic representation of sentences and phrases. Challenges and problems on Apps-based machine translation on smart devices towards AI, NLP, smart learning and understanding still remain until now, and need to be addressed and solved through collaboration between computational linguists and computer scientists.

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Section: Resultsmentioning

confidence: 99%

Apps-based Machine Translation on Smart Media Devices - A Review

Gunarto

2019

Indonesian J. Comput. Cybern. Syst.

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“…Figure 12 shows a confusion matrix for DGA multiclass classification. The confusion matrix allows us to deduce several other evaluation parameters such as the classification accuracy, classification error, sensitivity, precision and the f1-score [70][71][72][73][74][75].…”

Section: Confusion Matrix (Cm)mentioning

confidence: 99%

“…The classification accuracy gives us a measure of how often the classifier is correct [70][71][72][73][74][75][76]. Equation (7) gives the formula for calculating the classification accuracy.…”

Section: Classification Accuracymentioning

confidence: 99%

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A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

2021

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This study investigates the use of machine-learning approaches to interpret Dissolved Gas Analysis (DGA) data to find incipient faults early in oil-impregnated transformers. Transformers are critical pieces of equipment in transmitting and distributing electrical energy. The failure of a single unit disturbs a huge number of consumers and suppresses economic activities in the vicinity. Because of this, it is important that power utility companies accord high priority to condition monitoring of critical assets. The analysis of dissolved gases is a technique popularly used for monitoring the condition of transformers dipped in oil. The interpretation of DGA data is however inconclusive as far as the determination of incipient faults is concerned and depends largely on the expertise of technical personnel. To have a coherent, accurate, and clear interpretation of DGA, this study proposes a novel multinomial classification model christened KosaNet that is based on decision trees. Actual DGA data with 2912 entries was used to compute the performance of KosaNet against other algorithms with multiclass classification ability namely the decision tree, k-NN, Random Forest, Naïve Bayes, and Gradient Boost. Investigative results show that KosaNet demonstrated an improved DGA classification ability particularly when classifying multinomial data.

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“…Different smart meters, weather sensors or web APIs provide variate formats and recording rates that might be too detailed for certain analysis, therefore aggregation is necessary. Another challenge is related to the data quality since the data generated by smart meters or sensors could be missing, inconsistent or incomplete due to damages or communication interruptions [64]. When data quality issue is persistent, major prejudice to the STLF accuracy is caused.…”

Section: A Data Gathering Pre-processing and Storagementioning

confidence: 99%

Machine Learning Algorithms for Short-Term Load Forecast in Residential Buildings Using Smart Meters, Sensors and Big Data Solutions

Oprea

Bârã

2019

IEEE Access

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In this paper, we propose a scalable Big Data framework that collects the data from smart meters and weather sensors, pre-processes and loads it into a NoSQL database that is capable to store and further process large volumes of heterogeneous data. Then, a set of Machine Learning (ML) algorithms are designed and implemented to determine the load profiles and forecast the electricity consumption for residential buildings for the next 24 hours. For the Short-Term Load Forecast (STLF), a Feed-Forward Artificial Neural Network (FF-ANN) algorithm with backtracking adjustment of the learning rate that extends and optimizes the Nesterov learning method is proposed. Its performance is compared with six algorithms, i.e. FF-ANN with well-known learning methods, namely Momentum and Nesterov, Non-linear AutoRegressive with eXogenous (NARX), Deep Neural Network (DNN), Gradient Tree Boosting (GTB) and Random Forests (RF) that are competitive and powerful ML algorithms which have been successfully used for load forecast. Hence, for STLF, the seven algorithms are executed simultaneously and the best one is automatically selected considering its accuracy in terms of Root Mean Square Errors (RMSE). The proposed methodology contains the steps required to implement the Big Data framework, i.e. data pre-processing, transformation and loading, the configuration of the ML algorithms for dimensionality reduction, clustering, STLF with different algorithms from which the Best Performant Algorithm (BPA) is automatically selected to provide STLF for the next 24 hours. The methodology is ultimately tested considering a real case of a residential smart building.

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Internet of Things, Challenges for Demand Side Management

Cited by 7 publications

References 7 publications

Apps-based Machine Translation on Smart Media Devices - A Review

Apps-based Machine Translation on Smart Media Devices - A Review

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

Machine Learning Algorithms for Short-Term Load Forecast in Residential Buildings Using Smart Meters, Sensors and Big Data Solutions

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