scholarly journals HOUSEHOLD ELECTRICITY LOAD FORECASTING TOWARD DEMAND RESPONSE PROGRAM USING DATA MINING TECHNIQUES IN A TRADITIONAL POWER GRID

2021 ◽  
Vol 11 (4) ◽  
pp. 132-148
Author(s):  
Maher AbuBaker
Keyword(s):  
Data Mining ◽  
Demand Response ◽  
Power Grid ◽  
Load Forecasting ◽  
Data Mining Techniques ◽  
Demand Response Program ◽  
Electricity Load ◽  
Using Data ◽  
Electricity Load Forecasting

scholarly journals Electricity Load Forecasting Using Data Mining Technique

10.5772/48657 ◽  
2012 ◽  
Author(s):  
Intan Azmira binti Wan Abdul Razak ◽  
Shah bin ◽  
Mohd Shahrieel bin Mohd. Aras ◽  
Arfah binti
Keyword(s):  
Data Mining ◽  
Load Forecasting ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Electricity Load ◽  
Using Data ◽  
Electricity Load Forecasting

scholarly journals Various Electricity Load Forecasting Techniques with Pros and Cons

2020 ◽  
Vol 8 (6) ◽  
pp. 220-229
Keyword(s):  
Data Mining ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Geographic Area ◽  
Data Sets ◽  
Data Mining Techniques ◽  
Future Goals ◽  
Pros And Cons ◽  
Electricity Load ◽  
Stored Information

The rapid growth of stored information in the demand forecasting, associated with data analysis provoked an utmost need for generating a powerful tool which must be capable of extracting hidden and vital knowledge of load forecasting from available vast data sets. Being a promising sub domain of computer science, numerous data mining techniques suits the solution to this problem very well. This paper presents a vast, rigorous and comparable survey of tremendous data mining techniques useful in forecasting the electricity load demand of different geographic area. Based upon the rigorous survey, primary challenges involved in the current technologies and future goals are also discussed.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

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