Maximizing efficiency of energy consumption forecasting using a machine learning approach / (Record no. 175579)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 07207namaa22004451i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | EG-GICUC |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20251106165026.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251106s2024 ua a|||frm||| 000 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloguing agency | EG-GICUC |
| Language of cataloging | eng |
| Transcribing agency | EG-GICUC |
| Modifying agency | EG-GICUC |
| Description conventions | rda |
| 041 0# - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| Language code of summary or abstract | eng |
| -- | ara |
| 049 ## - Acquisition Source | |
| Acquisition Source | Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 006.31 |
| Edition number | 21 |
| 097 ## - Degree | |
| Degree | Ph.D |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Local Call Number | Cai01.18.05.Ph.D.2024.Mo.M |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Mohamed Mohamed Ramadan Ali Alsoul, |
| Preparation | preparation. |
| 245 10 - TITLE STATEMENT | |
| Title | Maximizing efficiency of energy consumption forecasting using a machine learning approach / |
| Statement of responsibility, etc. | by Mohamed Mohamed Ramadan Ali Alsoul ; Supervised Prof. Hegazy Mohamed Zaher, Prof. Naglaa Ragaa Saeid Hassan, Dr. Eman Mostafa Ahmed. |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | تعظيم كفاءة التنبؤ لاستهلاك الطاقة باستخدام منهجية تعلم الآله |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 91 Leaves : |
| Other physical details | illustrations ; |
| Dimensions | 30 cm. + |
| Accompanying material | CD. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rda content |
| 337 ## - MEDIA TYPE | |
| Media type term | Unmediated |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Source | rdacarrier |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (Ph.D)-Cairo University, 2024. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 85-91. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | Optimization extensively applied in various disciplines, ranging from engineering, computer science, finance, and decision-making. It is widely regarded as a fundamental technique with diverse applications. In the context of engineering and industry, optimization is employed to reduce costs and energy consumption or to maximize profits, output, performance and efficiency.<br/>Household energy consumption patterns are constantly evolving influenced by variables such as temperature, humidity and time of day. Predicting how much energy each family uses is really hard because everyone uses energy in different ways. Smart meters and home displays are on the rise. <br/>In this thesis, it was applied machine learning models for energy consumption in household and commercial sector, with a specific focus on identifying potential optimizations to reduce energy usage and maximize its efficiency, sustainability and preservation. With the emergence of machine learning, the ability to accurately predict future energy consumption has significantly reached applying two kinds of machine learning models. Computational intelligence methods showed better accuracy than statistical methods, especially for parameters with greater variability in source data. Thus, this thesis introduced two suggested models:<br/>The first model is about "Optimizing Homes Energy Consumption Utilization Using H-R Predictor" which it was applied using REFIT data collected from UK household smart meters to analyze energy consumption. <br/>The suggested prediction H-R algorithm is one of achievements of this thesis which it included two suggested unsupervised machine learning models (LSTM and XGBoost) to train data for forecasting and controlling the energy consumption. The performance of these models was evaluated using R2 as a metric to measure the percentage of variance in the dependent variable that can be predicted.<br/><br/>The second model is about "A hybrid Firefly based Gravity Search Algorithm (F-GSA) for Optimizing Energy Consumption". The proposed model was developed to forecast the energy consumption in homes.<br/>Hybrid firefly-based Gravity Search Algorithm (F-GSA) enhance energy consumption predictions by combining strengths, providing efficient projections and historical data insights, offering a promising solution for tackling energy consumption forecasting challenges.<br/>The research models were assessed using root mean squared error (RMSE), mean absolute deviation (MAD) and R-Squared (R²), also known as the coefficient of determination, in order to directly compare them to the energy readings in the data. MAD is utilized to quantify the error in predicting energy consumption in 10-minute intervals (equivalent to six errors per hour). On the other hand, R² is employed to evaluate the overall performance of the models in predicting energy consumption over a specific time period, such as a day or month. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | يتم تطبيق الأمثلية (Optimization) على نطاق واسع في مختلف التخصصات مثل الإقتصاد والهندسة وعلوم الكمبيوتر والمصارف المالية وغيرها بهدفتعظيم الربحية أو تقليل التكاليف والمخاطر أو المزيج بين كلاهما. وتتضمن الامثلية تطبيقات متنوعة ومعقدة ومن ثم تحتوي على عدد كبير من المعلمات (Parameters) هكذا يصعب حل مثل هذه المشكلات بالطرق التقليدية. تقدم خوارزميات التعلم الآلي ML وخوارزميات Metaheuristic حلول جيدة وفعالة قريبة من المثالية من خلال البحث بذكاء فى مساحة الحل لتلك الأنواع من المشكلات.<br/>ومع تطور أنماط استهلاك الطاقة المنزلية باستمرار متأثرة بمتغيرات مثل درجة الحرارة والرطوبة خلال فترات اليوم أو خلال الفترات الموسمية يعتبر التنبؤ بكمية الطاقة التي تستخدمها كل أسرة أمرًا صعبًا لأن الجميع يستخدم الطاقة بطرق مختلفة. يهدف هذا البحث إلى استكشاف مجال استهلاك الطاقة الكهربائية في القطاع المنزلي والتجاري من خلال التعلم الآلي (ML) مع التركيز بشكل خاص على تحديد التحسينات المحتملة لتقليل استخدام الطاقة وتعظيم كفاءتها واستدامتها والحفاظ عليها. <br/>مع ظهور طرق التعلم الآلي واستخدام العدادات الذكية (Smart meters) تحسنت القدرة على التنبؤ باستهلاك الطاقة في المستقبل بدقة عالية وبشكل كبير نتيجه استخدام طرق الذكاء الإصطناعى وتعلم الالة لتدريب النماذج عليها واستخدامها كأداه قوية للتنبؤ حيث تظهر طرق الذكاء الاصطناعى دقة أفضل من الطرق الإحصائية التقليدية وخاصة بالنسبة للمعلمات ذات التباين الأكبر في بيانات المصدر |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Issues CD | Issues also as CD. |
| 546 ## - LANGUAGE NOTE | |
| Text Language | Text in English and abstract in Arabic & English. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | تعلم الآله |
| 653 #1 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Energy Consumption Forecasting |
| -- | Machine Learning Models |
| -- | Predictive Analytics |
| -- | Time Series Analysis |
| -- | Feature Engineering |
| -- | Data Preprocessing |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Hegazy Mohamed Zaher |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Naglaa Ragaa Saeid Hassan |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Eman Mostafa Ahmed |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2024 |
| Supervisory body | Hegazy Mohamed Zaher |
| -- | Naglaa Ragaa Saeid Hassan |
| -- | Eman Mostafa Ahmed |
| Universities | Cairo University |
| Faculties | Faculty of Graduate Studies for Statistical Research |
| Department | Department of Operations Research & Management |
| 905 ## - Cataloger and Reviser Names | |
| Cataloger Name | Shimaa |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| Edition | 21 |
| Suppress in OPAC | No |
| Source of classification or shelving scheme | Home library | Current library | Date acquired | Inventory number | Full call number | Barcode | Date last seen | Effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | قاعة الرسائل الجامعية - الدور الاول | 06.11.2025 | 92377 | Cai01.18.05.Ph.D.2024.Mo.M | 01010110092377000 | 06.11.2025 | 06.11.2025 | Thesis |