Decision Tree Based Customer Analysis Method for Energy Planning in Smart Cities

2020 ◽  
Author(s):  
Orhan Yaman ◽  
Hasan Yetis ◽  
Mehmet Karakose
Keyword(s):  
Decision Tree ◽  
Smart Cities ◽  
Energy Planning ◽  
Analysis Method ◽  
Customer Analysis

scholarly journals Short-term Load Forecasting Model Considering Meteorological Factors

2019 ◽  
Vol 118 ◽  
pp. 02050
Author(s):  
Xi Yunhua ◽  
Zhu Haojun ◽  
Dong Nan
Keyword(s):  
Decision Tree ◽  
Meteorological Factors ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Analysis Method ◽  
Decision Tree Algorithm ◽  
Characteristic Analysis ◽  
Short Term ◽  
Tree Algorithm ◽  
Short Term Load Forecasting

Because of the limitation of basic data and processing methods, the traditional load characteristic analysis method can not achieve user-level refined prediction. This paper builds a user-level short-term load forecasting model based on algorithms such as decision trees and neural networks in big data technology. Firstly, based on the grey relational analysis method, the influence of meteorological factors on load characteristics is quantitatively analyzed. The key factors are selected as input vectors of decision tree algorithm. This paper builds a category label for each daily load curve after clustering the user’s historical load data. The decision tree algorithm is used to establish classification rules and classify the days to be predicted. Finally, Elman neural network is used to predict the short-term load of a user, and the validity of the model is verified.

scholarly journals RUS Boost Tree Ensemble Classifiers for Occupancy Detection

2019 ◽  
Vol 8 (2S2) ◽  
pp. 272-277
Keyword(s):  
Decision Tree ◽  
Smart Cities ◽  
Ensemble Classifier ◽  
Sensor Data ◽  
Ensemble Classifiers ◽  
Humidity Ratio ◽  
Accuracy Rate ◽  
Weak Learner ◽  
Occupancy Detection ◽  
Save Energy

In this research paper, various ensemble classifiers are used to predict occupancy status using samples of light, temperature, humidity, CO2 , humidity ratio sensor data. Occupancy detection will save energy making room for smart buildings in smart cities. It paves ways to decide on heating, ventilation, cooling and lighting. To achieve 'white box' output and facilitate explanatory interpretation, decision tree was employed, Several weak learner decision trees were melded to form RUSBoosted Tree ensemble classifier. On investigation of the results, it is seen that RUSBoostedTree Ensemble gives the highest accuracy rate of 99%

scholarly journals Tackling pandemics in smart cities using machine learning architecture

2021 ◽  
Vol 18 (6) ◽  
pp. 8444-8461
Author(s):  
Desire Ngabo ◽  
◽  
Wang Dong ◽  
Ebuka Ibeke ◽  
Celestine Iwendi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Logistic Regression ◽  
Decision Tree ◽  
Naive Bayes ◽  
Smart Cities ◽  
Naïve Bayes ◽  
Healthcare Data ◽  
Runny Nose ◽  
Rate Analysis ◽  
Nearest Neighbours

<abstract><p>With the recent advancement in analytic techniques and the increasing generation of healthcare data, artificial intelligence (AI) is reinventing the healthcare system for tackling pandemics securely in smart cities. AI tools continue register numerous successes in major disease areas such as cancer, neurology and now in new coronavirus SARS-CoV-2 (COVID-19) detection. COVID-19 patients often experience several symptoms which include breathlessness, fever, cough, nausea, sore throat, blocked nose, runny nose, headache, muscle aches, and joint pains. This paper proposes an artificial intelligence (AI) algorithm that predicts the rate of likely survivals of COVID-19 suspected patients based on good immune system, exercises and age quantiles securely. Four algorithms (Naïve Bayes, Logistic Regression, Decision Tree and k-Nearest Neighbours (kNN)) were compared. We performed True Positive (TP) rate and False Positive (FP) rate analysis on both positive and negative covid patients data. The experimental results show that kNN, and Decision Tree both obtained a score of 99.30% while Naïve Bayes and Logistic Regression obtained 91.70% and 99.20%, respectively on TP rate for negative patients. For positive covid patients, Naïve Bayes outperformed other models with a score of 10.90%. On the other hand, Naïve Bayes obtained a score of 89.10% for FP rate for negative patients while Logistic Regression, kNN, and Decision Tree obtained scores of 93.90%, 93.90%, and 94.50%, respectively.</p></abstract>

scholarly journals Infrastructure Elements for Smart Campuses: A Bibliometric Analysis

2021 ◽  
Vol 13 (14) ◽  
pp. 7960
Author(s):  
Temitope Omotayo ◽  
Alireza Moghayedi ◽  
Bankole Awuzie ◽  
Saheed Ajayi
Keyword(s):  
Bibliometric Analysis ◽  
Smart Cities ◽  
Building Construction ◽  
It Project Management ◽  
Analysis Method ◽  
Teaching Management ◽  
Learning And Teaching ◽  
Smart Learning ◽  
Smart Campus ◽  
Procurement Strategy

Sustainable development can be attained at a microlevel and having smart campuses around the world presents an opportunity to achieve city-wide smartness. In the process of attaining smartness on campuses, the elements requiring attention must be investigated. There are many publications on smart campuses, and this investigation used the bibliometric analysis method to identify such publications produced over the last decade. A matrix of 578 nodes and 3217 edges was developed from 285 publications on smart campus construction and procurement. Fifteen cluster themes were produced from the bibliometric analysis. The findings revealed that China contributed 48.4% of all published articles on the smart campus. The findings presented a framework from the cluster themes under the four broad infrastructure areas of building construction or repurposing, technology and IT network, continuous improvement, and smart learning and teaching management. The implications of the findings identified that IT project management, traditional procurement strategy, and standard forms of contracts such as the New Engineering Contract (NEC) and the Joint Contract Tribunal (JCT) are applicable in the procurement of smart cities.

scholarly journals Energy Planning and Energy Efficiency in Smart City Areas

2019 ◽  
Vol 61 ◽  
pp. 01017 ◽  
Author(s):  
Yury R. Nurulin ◽  
Inga V. Skvortsova ◽  
Olga A. Kalchenko
Keyword(s):  
Energy Efficiency ◽  
Smart City ◽  
Smart Cities ◽  
Value Added ◽  
Energy Planning ◽  
Improve Energy Efficiency ◽  
Joint Development ◽  
Urban Structures ◽  
European Cities ◽  
Innovative Concept

The main value added of the approach, which is considered in this paper, is the joint development of an innovative concept for energy improvement city's areas, as well as methods and tools for its implementation. A new coordinated approach to energy planning and implementation at the district level within the framework of the concept of smart cities contributes to the efforts of consumers to improve energy efficiency. The research focuses on energy efficiency for existing built-up urban structures. They represent a large part of the built environment of European cities and face significant, often urgent energy challenges.

scholarly journals PEDESTRIAN DEAD RECKONING USING SMARTPHONES SENSORS: AN EFFICIENT INDOOR POSITIONING SYSTEM IN COMPLEX BUILDINGS OF SMART CITIES

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 905-912
Author(s):  
E. Saadatzadeh ◽  
A. Chehreghan ◽  
R. Ali Abbaspour
Keyword(s):  
Decision Tree ◽  
Smart Cities ◽  
Dead Reckoning ◽  
Principal Component ◽  
Indoor Positioning ◽  
Sensor Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Mean ◽  
Pedestrian Dead Reckoning

Abstract. This paper proposes an indoor positioning method using Pedestrian Dead Reckoning (PDR) based on the detection of the mode of the user’s smartphone. In the first step, to determine the mode of carrying the smartphone (Holding, Calling, Swinging) by suitably formed feature vectors based on sensor data, three classification algorithms (Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN)) are evaluated. From the classification algorithm perspective, the decision tree algorithm had the best performance in terms of processing time and classification. Secondly, to determine the user position, the step detection is performed by defining the upper threshold and time threshold for Acceleration norm values. The orientation component is obtained by combining accelerometer, magnetometer, and gyroscope data using Complementary Filtering and Principal Component Analysis based on Global Acceleration (PCA-GA) methods. The mean standard deviation along the direct path for the three modes of carrying (Holding, Calling, and Swinging) were obtained 6.22, 6.82, and 14.68 degrees, respectively. Localization experiments were performed on 3 modes of carrying a smartphone in a rectangular geometry path. The mean final error of positioning from ordinary walking for the three modes of holding (Calling, Holding, Swinging) were obtained 2.11, 2.34, and 4.5 m, respectively.

scholarly journals Intelligent Medical Auxiliary Diagnosis Algorithm Based on Improved Decision Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yuntao Wei ◽  
Xiaojuan Wang ◽  
Meishan Li
Keyword(s):  
Decision Tree ◽  
Classification Performance ◽  
Optimal Decision ◽  
Analysis Method ◽  
Analysis Model ◽  
Tree Model ◽  
Pathological Characteristics ◽  
Adaptive Classification ◽  
Dependent Variables ◽  
Physiological Indexes

In order to address the problem of low ability of intelligent medical auxiliary diagnosis (IMAD), an IMAD based on improved decision tree is proposed. Firstly, the constraint parameter model of IMAD is constructed. Secondly, according to the physiological indexes of IMAD, the independent variables and dependent variables of auxiliary diagnosis are constructed, the quantitative recurrent analysis of IMAD is carried out by using regression analysis method, the data analysis model of IMAD is constructed, and the adaptive classification and recognition of IMAD are carried out. Finally, the attribute feature quantity of IMAD with pathological characteristics is extracted, and the improved decision tree model is used to realize intelligent medical auxiliary, assist in the optimal decision of diagnosis, and realize the effective classification and recognition of pathological characteristics. The results show that this method has better decision-making ability and better classification performance for IMAD, which improves the intelligence and accuracy of intelligent medical auxiliary diagnosis.

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