scholarly journals Predict Electric Power Demand with Extended Goal Graph and Heterogeneous Mixture Modeling

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 134 ◽  
Author(s):  
Noriyuki Kushiro ◽  
Ami Fukuda ◽  
Masatada Kawatsu ◽  
Toshihiro Mega
Keyword(s):  
Linear Regression ◽  
Energy Demand ◽  
Mixture Modeling ◽  
Data Partitioning ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Heterogeneous Mixture ◽  
Energy Prediction ◽  
Consumption Data ◽  
Goal Graph

In this study, methods for predicting energy demand on hourly consumption data are established for realizing an energy management system for buildings. The methods consist of an energy prediction algorithm that automatically separates the datasets to partitions (gate) and creates a linear regression model (local expert) for each partition on the heterogeneous mixture modeling, and an extended goal graph that extracts candidates of variables both for data partitioning and for linear regression for the energy prediction algorithm. These methods were implemented as tools and applied to create the energy prediction model on two years' hourly consumption data for a building. We validated the methods by comparing accuracies with those of different machine learning algorithms applied to the same datasets.

scholarly journals Forecast and Analysis of Regional Energy Demand Based on Grey Linear Regression Forecast Model

2021 ◽  
Vol 645 ◽  
pp. 012047
Author(s):  
Mingli Zhang ◽  
Zhongwei Li ◽  
Kun Song ◽  
Duojiao Guan ◽  
Na Zhang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Energy Demand ◽  
Forecast Model ◽  
Regional Energy ◽  
Regional Energy Demand

Improving Customer Value Index and Consumption Forecasts using a Weighted RFM Model and Machine Learning Algorithms

2022 ◽  
Vol 30 (3) ◽  
pp. 0-0
Keyword(s):  
Customer Value ◽  
Marketing Strategies ◽  
Machine Learning Algorithms ◽  
Entropy Weight ◽  
Rfm Model ◽  
Consumption Data ◽  
Entropy Weight Method ◽  
Customer Profiling ◽  
Weight Method ◽  
Rmf Model

Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies.

scholarly journals Two-phased DEA-MLA approach for predicting efficiency of NBA players

2014 ◽  
Vol 24 (3) ◽  
pp. 347-358 ◽  
Author(s):  
Sandro Radovanovic ◽  
Milan Radojicic ◽  
Gordana Savic
Keyword(s):  
Linear Regression ◽  
High Reliability ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Efficiency Frontier ◽  
Function Form ◽  
Vector Machines ◽  
Challenging Tasks ◽  
Dea Analysis

In sports, a calculation of efficiency is considered to be one of the most challenging tasks. In this paper, DEA is used to evaluate an efficiency of the NBA players, based on multiple inputs and multiple outputs. The efficiency is evaluated for 26 NBA players at the guard position based on existing data. However, if we want to generate the efficiency for a new player, we would have to re-conduct the DEA analysis. Therefore, to predict the efficiency of a new player, machine learning algorithms are applied. The DEA results are incorporated as an input for the learning algorithms, defining thereby an efficiency frontier function form with high reliability. In this paper, linear regression, neural network, and support vector machines are used to predict an efficiency frontier. The results have shown that neural networks can predict the efficiency with an error less than 1%, and the linear regression with an error less than 2%.

scholarly journals A Review on Linear Regression Comprehensive in Machine Learning

2020 ◽  
Vol 1 (4) ◽  
pp. 140-147
Author(s):  
Dastan Maulud ◽  
Adnan M. Abdulazeez
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Linear Relationship ◽  
Multiple Regression ◽  
Polynomial Regression ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Explanatory Variables ◽  
Almost All ◽  
Simple Regression

Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

scholarly journals Prediction Model for Occupational Incidents in Chemical and Gas Industries

2019 ◽  
Vol 8 (4) ◽  
pp. 3836-3840
Keyword(s):  
Prediction Model ◽  
Technical Information ◽  
Workplace Safety ◽  
Machine Learning Algorithms ◽  
Streaming Data ◽  
Industrial Safety ◽  
Prediction Algorithm ◽  
Scoring Algorithm ◽  
Inspection Data ◽  
The Right

Understanding occupational incidents is one of the important measures in workplace safety strategy. Analyzing the trends of the occupational incident data helps to identify the potential pain points and helps to reduce the loss. Optimizing the Machine Learning algorithms is a relatively new trend to fit the prediction model and algorithms in the right place to support human beneficial factors. The aim of this research is to build a prediction model to identify the occupational incidents in chemical and gas industries. This paper describes the architecture and approach of building and implementing the prediction model to predict the cause of the incident which can be used as a key index for achieving industrial safety in specific to chemical and gas industries. The implementation of the scoring algorithm coupled with prediction model should bring unbiased data to obtain logical conclusion. The prediction model has been trained against FACTS (Failure and Accidents Technical information system) is an incidents database which have 25,700 chemical industrial incidents with accident descriptions for the years span from 2004 to 2014. Inspection data and sensor logs should be fed on top of the trained dataset to verify and validate the implementation. The outcome of the implementation provides insight towards the understanding of the patterns, classifications, and also contributes to an enhanced understanding of quantitative and qualitative analytics. Cutting edge cloud-based technology opens up the gate to process the continuous in-streaming data, process it and output the desired result in real-time. The primary technology stack used in this architecture is Apache Kafka, Apache Spark Streaming, KSQL, Data frames, and AWS Lambda functions. Lambda functions are used to implement the scoring algorithm and prediction algorithm to write out the results back to AWS S3 buckets. Proof of concept implementation of the prediction model helps the industries to see through the incidents and will layout the base platform for the various safety-related implementations which always benefits the workplace's reputation, growth, and have less attrition in human resources.

Patient visit forecasting in an emergency department using a deep neural network approach

Kybernetes ◽  
2019 ◽  
Vol 49 (9) ◽  
pp. 2335-2348 ◽  
Author(s):  
Milad Yousefi ◽  
Moslem Yousefi ◽  
Masood Fathi ◽  
Flavio S. Fogliatto
Keyword(s):  
Neural Network ◽  
Emergency Department ◽  
Linear Regression ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Neural Network Approach ◽  
Content Type

Purpose This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days. Design/methodology/approach In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor. Findings Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression. Research limitations/implications The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications. Originality/value To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.

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