Energy Smart Meter operation improved by Machine Learning’s Decision-Support System and Internet Of

GRD Journals | Global Research and Development Journal for Engineering | National Conference on Advancement in Emerging Technologies (NCAET'18) | March 2018

e-ISSN: 2455-5703

Energy Smart Meter operation improved by Machine Learning’s Decision-Support System and Internet of Things 1Gomathi.

S 2Suruthi. R. S 3Yamuna. S 1,2,3 UG Scholar 1,2,3 Department of Electronics and Communication Engineering 1,2,3 363, Arcot Road, Kodambakkam, Chennai, Tamil Nadu, India-600024 Abstract The electricity has become a part of daily life, which plays an important role in our homes and industries. The system is now focused on the growing demand of power and the need of finding the alternative energy source. The idea of a ‘smart city’ is the key solution to these power related problems, giving us a futuristic scope. Better understanding of domestic and commercial energy usage brings with it a problem of managing and classifying the sheer amount of data that comes along with it. The work proposal is basically to overcome the demand of power using smart meter in electric power consumption benefiting customer to monitor and manage the electric power usage. This idea is made easier by applying Machine Learning’s. Decision Support System an application of Artificial Intelligence (AI) to classify and distribute energy while managing and enhancing the other supporting features of an Electric S mart Meter (ES M) using Internet of Things (IOT). We plan on introducing smart meters as a ‘live’ communication tool connecting the provider with its customers, which will cause the electrical network industry to face a 360 degree turn around towards a customer-centric business. The system employs the Bayesian Network (BN) prediction model with the three machine learning model that is Naïve Bayes (NB), Decision Tree (DT) and Random Forest (RT). The ES M systems network model is based on the four cornerstones fundamental to IOT: sensing, computing, communication, and actuation.

Keyword- ESM, BN, NB, DT, Gini Index, RF, IOT __________________________________________________________________________________________________

I. INTRODUCTION The Electric Smart Meter business segment is rapidly expanding throughout the utilities domain, with increased use of smart meters and smart grids, in smart city. The ESM will occupy the largest share, with more than 70% of all installed smart meter bases. Smart devices are cyber-physical systems that contain both hardware and software, and represent a largely interconnected ecosystem.

This ecosystem is complex to operate, and new methods will be required to manage it and use the vast amounts of data it generates.

Decision models that are sensitive to transient network dynamics will be required to assist in this process and ensure ESM system cost efficiency [1]. We propose a Bayesian model, which is driven by continuous analysis of ecosystem communication quality.

Fig.1 represents the Bayesian ecosystem of an ESM. Like all infrastructure related problems, even in the power sector, the problem lies with capacity. We do not have enough capacity to generate enough power when it is needed. And every time we add capacity, it becomes insufficient as the demand increases, which results in power cuts. Our proposal deals with the distribution and aims at supplying power to all the connected loads even in this power deficit situation using the decisions made by the machine learning classifiers.

Fig. 1: Bayesian Ecosystem of an ESM

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Energy Smart Meter operation improved by Machine Learning’s Decision-Support System and Internet of Things (GRDJE / CONFERENCE / NCAET - 2018 / 002)

II. METHODOLOGY The power distribution and its various connected components are controlled using a microcontroller.

Fig. 2: Proposed system flow

There are three major procedures used in the power distribution. The proposed system flow for power distribution is seen in Fig.2.

III. DATA SENSING The ESM ecosystem involves data monitoring of power generated from the source and sensing the power requirement of various loads connected. Sensing of the power parameters within the grid is done using the current and voltage transformers, which communicates with the microcontroller.

IV. DATA PREPROCESSING In the data processing stage, the consumed data is selected from the retrieved data; the mean, standard deviation and skewness of the consumed data based on power consumptions are computed using standard formulas.

V. DATA CLASSIFICATION Classification is the issue of distinguishing the category to which the present data belongs, based on the knowledge gained from the training data set. In the proposition machine learning is utilized to classify the various power values obtained from the sensors relaying on the training data set available for the system.

A. Bayesian Network A Bayesian Network represents the causal probabilistic relationship among a set of random variables, their conditional dependences, and it provides a compact representation of a joint probability distribution. It consists of two major parts: a directed acyclic graph and a set of conditional probability distributions [2].

B. Naïve Bayes Naïve Bayes classifier is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. It is probabilistic classifier based on prior probability and the class model. Naïve Bayes is a generative model which constructs models for each class. The Bayesian probability of a class is given by,

(1)

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Energy Smart Meter operation improved by Machine Learning’s Decision-Support System and Internet of Things (GRDJE / CONFERENCE / NCAET - 2018 / 002)

Where P(Ck|x) is the posterior probability of class, P(Ck) is the prior probability of class, P(x|Ck) is the class - conditional probability, P(x) is the prior probability of predictor [3]. The data can be normalized to a Gaussian distribution for continuous values in the data by using the following equation,

(2) The Naïve Bayes classifier algorithm proceeds as follows, – The given training data set is converted into a frequency table.

– A likelihood table is generated by calculating the probabilities of each parameter.

– Using Bayesian equation, the posterior probability is computed for each class. The label with highest posterior probability is the prediction output of the classifier.

C. Decision Tree A Decision Tree is a graph that uses a branching method to illustrate every possible outcome of a decision. Decision Trees can be used to assign monetary, time or other values to possible outcomes so that decisions can be automated. Decision Tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subs ets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. Decision Tree is used in data mining to simplify complex strategic challenges and evaluate the cost effectiveness of research decisions.

The growing of a Decision Tree involves selecting features, choosing criteria for splitting, knowing when to stop and using pruning technique to reduce the size of the tree.

D. Gini Index Gini index is a method used for choosing splitting criteria of the tree. The Gini index is used to compute the impurity of a data partition. In Classification and Regression Tree, we perform binary splits. So the Gini index will be computed as the weighted sum of the resulting partitions and we select the split with the smallest Gini index.

Gini index is given by the below formula where pi represent proportion of class inputs present in a particular group.

(3) Gini index gives higher value for higher homogeneity. Maximum value of Gini Index could be when all target values are equally distributed. Minimum value of Gini Index will be 0 when all observations belong to one label [4].

Gini index is intended for continuous attributes, will tend to find the largest of the class, minimizes misclassifications and is quite faster to compute.

E. Random Forest Random Forest is a tree-based algorithm which involves building several Decision Trees, then combining their output to improve generalization ability of the model. The method of combining trees is known as an ensemble method. Ensemble is the combination of weak learner i.e. individual trees to produce a strong learner.

In the Random Forest approach, a large number of decision trees are created. Every observation is fed into every Decision Tree. The most common outcome for each observation is used as the final output. A new observation is fed into all the trees and taking the majority vote or by calculating the mean for each classification model [5].

VI. DISTRIBUTION OF POWER FROM MICROCONTROLLER To distribute the power to the demanded node while making the other nodes to operate at low power, the system has to consider the availability of all the nodes and their power requirements. Based on this analysis the system makes decisions considering the availability of generated power as well to fix the threshold for power distribution. From these classified data the decisions on which load to have how much supply of power is known and this information are fed into the microcontroller. The microcontroller triggers the relay which supplies the required power to the demanded load while functioning the others nodes as well.

VII.

IMPLICATION OF IOT

The vision of IOT can be seen from two perspectives Internet centric and Thing centric. The Internet centric architecture will involve internet services being the main focus while data is contributed by the objects. In the object centric architecture, the smart Objects take the center stage. A conceptual framework integrating the ubiquitous sensing devices and the applications is visualized.

In order to realize the full potential of cloud computing as well as ubiquitous Sensing, a combined framework with a cloud at the center seems to be most viable. This not only gives the flexibility of dividing associated costs in the most logical manner but is

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Energy Smart Meter operation improved by Machine Learning’s Decision-Support System and Internet of Things (GRDJE / CONFERENCE / NCAET - 2018 / 002)

also highly scalable. Sensing service providers can join the network and offer their data using a storage cloud; analytic tool developers can provide their software tools; artificial intelligence experts can provide their data mining and machine learning tools useful in converting information to knowledge and finally computer graphics designer can offer a variety of visualization tools [6].

VIII. RESULT The work proposal based in python program on Bayesian Network with the Naïve Bayes, Decision Tree and Random Forest is thus analyzed on energy consumption grounds. The calculated efficiency for the classifiers is 95% for Random Forest, 90% for Decision Tree and 88% for Naives Bayes.

Fig. 3: Comparison of classifiers

IX. FUTURE SCOPE AND CONCLUSION This project presents a comprehensive description about Energy Smart Meter system and enhancing power distribution from it. In our work, we address how efficient an electric meter can be used and how the concept of “Smart city” has evolved over time. We examine the challenges and applications of ESM in power distribution from the point of view of both the consumer and the distributer. The work explains how combining Machine Learning an application of AI and embedded systems to a simple smart meter makes it efficient and useful.

X. FUTURE ENHANCEMENT This project can be molded into complete smart city equipment for power usage and enhancement, with further development in various criteria. The Decision Support System can be improved with better Machine Learning classifiers in the future.

We have considered two design oriented criteria for the enhancement of our model best known to our knowledge. Firstly meter health monitoring can be included in the model. Using temperature and vibration sensors and notifying the defect detected by the model can enhance the performance of the meter.

Secondly monitoring of the micro grid generation and its feed in back tariff, if the ecosystem uses one will be an additional feature.

REFERENCES [1] [2] [3] [4] [5] [6] [7]

Joseph Siryani, Ph.D. Candidate, Bereket Tanju, Ph.D., and Timothy Eveleigh, D.Sc (2017) -A Machine Learning Decision Support System Improves the Internet of Things’ Smart Meter Operations. http://www.bu.edu/sph/files/2014/05/bayesian-networks-final.pdf http://chemeng.utoronto.ca/~datamining/dm c/decision_tree.htm http://dni-institute.in/blogs/cart-decision-tree-gini-index-exp lained/ http://www.tutorialspoint.com/r/r_random_f orest.htm http://www.cloudbus.org/papers/Internet-of-Things -Vision-Future2012.pdf

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