Computational Cost Analysis and Data-Driven Predictive Modeling of Cloud-based Online NILM Algorithm
ABSTARCT :
Online non-intrusive load monitoring methods have captivated academia and industries as parsimonious solutions for household energy efficiency monitoring as well as safety control, anomaly detection, and demand-side management. However, despite the promised energy efficiency by providing appliance specific consumption information feed-backs, the computational energy cost for running the load monitoring systems is not explored. This study analyzes whether the energy spent to execute the non-intrusive algorithms, out-weights the expected energy efficiency gain from using the algorithms. Furthermore, we present a study on the computation costs estimation and prediction of a Cloud-based online non-intrusive load disaggregation algorithm through data-driven models. Moreover, a generic framework for an automated algorithm computational cost monitoring and the modeling methodologies are devised and proposed for meeting extensive scaling load monitoring and deployment requirements. The proposed approach was examined and validated on ls and cvs running the disaggregation algorithm. The prediction models, developed using statistical and machine learning tools, demonstrate the promising applicability of the data-driven approach with a very high prediction accuracy without detailed knowledge of the computing systems and the algorithm.
EXISTING SYSTEM :
? This data acquisition is commonly related to a device or system, very close to the existing electrical facilities, where different approaches can be deployed in order to measure certain parameters, such as currents or voltages, in a certain household or building.
? It has been concluded that all models provide excellent classification performance and correctly identified the existing devices, establishing the applicability of the proposed approach.
? The first step of any HEMS is to monitor the electricity consumption of the several devices existing in a household.
? As a myriad of approaches has been proposed for this last step of NILM, the aim of this section is not to provide a deep review of the existing alternatives, but rather to point out important works on optimization and machine learning (supervised and unsupervised) algorithms used for load classification.
DISADVANTAGE :
? These power changes are grouped into clusters, with each cluster representing a state change of a target appliance. Since then, several works have investigated the NILM problem utilizing different sampling rates and techniques.
? This visual representation solves the appliance recognition problem by exploiting computer vision techniques.
? Thus, there is no need to implement specific algorithms; instead, by training a CNN model, the classification problem can be successfully solved.
? To evaluate the classifier, the most common metrics used in classification and NILM problems are adopted.
PROPOSED SYSTEM :
• In an event-based algorithm is proposed to identify load signatures, according to trajectories of real, reactive and distortion power.
• Other transforms were also employed, such as the Stockwell Transform , and combinations of different techniques, such as DWT and harmonics , have also been proposed.
• A fusion of a supervised training process over available labelled datasets with an unsupervised training method over unlabelled aggregate data is proposed in.
• The authors analysed the requirements of DR and proposed a new NILM system with an enhanced load space and measurement approach.
• As their application did not include renewables, they proposed to include them, together with a forecasting mechanism for the electricity produced, in future work.
ADVANTAGE :
? In this sense, the proposed algorithm performance may degrade for multi-state appliances.
? Consequently, the identification algorithm’s performance is independent of the simultaneous operation of other types of appliances, even when a large number of devices is considered.
? It is evident that the proposed classification algorithm presents high performance regarding the microwave and the fridge.
? The overall performance of the proposed methodology is tested on a private dataset.
? The performance of the proposed methodology is compared to other NILM-based energy consumption estimation systems.
? In this study, three appliances are selected to test the proposed methodology’s performance, i.e., fridge, washing machine, and microwave oven.
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