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040			_aEG-GICUC _beng _cEG-GICUC _dEG-GICUC _erda
041	0		_aeng _beng _bara
049			_aDeposit
082	0	4	_a006.31
092			_a006.31 _221
097			_aPh.D
099			_aCai01.18.05.Ph.D.2024.Mo.M
100	0		_aMohamed Mohamed Ramadan Ali Alsoul, _epreparation.
245	1	0	_aMaximizing efficiency of energy consumption forecasting using a machine learning approach / _cby Mohamed Mohamed Ramadan Ali Alsoul ; Supervised Prof. Hegazy Mohamed Zaher, Prof. Naglaa Ragaa Saeid Hassan, Dr. Eman Mostafa Ahmed.
246	1	5	_aتعظيم كفاءة التنبؤ لاستهلاك الطاقة باستخدام منهجية تعلم الآله
264		0	_c2024.
300			_a91 Leaves : _billustrations ; _c30 cm. + _eCD.
336			_atext _2rda content
337			_aUnmediated _2rdamedia
338			_avolume _2rdacarrier
502			_aThesis (Ph.D)-Cairo University, 2024.
504			_aBibliography: pages 85-91.
520		3	_aOptimization 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. 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. 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: 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. 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. 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. 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. 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	_aيتم 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. 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. 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			_aIssues also as CD.
546			_aText in English and abstract in Arabic & English.
650		0	_aMachine learning
650		0	_aتعلم الآله
653		1	_aEnergy Consumption Forecasting _aMachine Learning Models _a Predictive Analytics _aTime Series Analysis _aFeature Engineering _aData Preprocessing
700	0		_aHegazy Mohamed Zaher _ethesis advisor.
700	0		_aNaglaa Ragaa Saeid Hassan _ethesis advisor.
700	0		_aEman Mostafa Ahmed _ethesis advisor.
900			_b01-01-2024 _cHegazy Mohamed Zaher _cNaglaa Ragaa Saeid Hassan _cEman Mostafa Ahmed _UCairo University _FFaculty of Graduate Studies for Statistical Research _DDepartment of Operations Research & Management
905			_aShimaa
942			_2ddc _cTH _e21 _n0
999			_c175579