Analyzing the Impacts of a Deep-Learning Based Day-Ahead Residential Demand Response Model on The Jordanian Power Sector in Winter Season

The ground elevation is an important site-specific parameter for civil engineering. LiDAR is used to determine Ground Elevation at the dawn of new technology; however, there are disadvantages to using LiDAR. The Ground Elevation is also measured during a Geotechnical Site Investigation, however, these data were only collected at the project's location, leaving unknown at other locations. In order to address this issue, machine learning models were used to generate the ground elevation for selected locations, in this case, Metro Manila Philippines. Models of machine learning were trained: Linear Regression Model, Quadratic Regression Model, Tree Regression Model, Boosted Trees Model, and Artificial Neural Network. The Tree Regression Model is the winning model, and its hyperparameter was optimized. To validate, the generated ground elevation was compared to the collected Metro Manila Digital Terrain Model (DTM).

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“…Using Machine Learning Models to predict ground elevation is incredibly useful. It is suggested to apply it to other fields as well [36][37].…”

Section: Discussionmentioning

confidence: 99%

Estimating Ground Elevation using Borehole Information: A Case of Metro Manila, Philippines

Galupino¹,

Dungca²

2022

Proceedings of International Exchange and Innovation Conference on Engineering &Amp; Sciences (IEICES)

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“…Building energy demand is a multidimensional problem that can be approached considering different intricate paradigms listed below: 1-Improvement of building thermal performance determined by materials and design of a building [3]. 2-Energy efficiency improvement of appliances and facilities [4] 3-Changes in energy-related occupant behaviors through various interventions, including marketbased pricing policies and the demand response approach [5][6]. 4-Renewable energy integration [7].…”

Section: Proceedings Of the 8 Th International Exchange And Innovatio...mentioning

confidence: 99%

Recent Advances in Reinforcement Learning Applications for Building Energy Management: A Mini Review.

Shaqour¹,

Hagishima²

2022

Proceedings of International Exchange and Innovation Conference on Engineering &Amp; Sciences (IEICES)

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In 2019, buildings accounted for 55% of the global electricity demand, making them a key contributor to global emissions and a core target for energy efficiency, energy reduction, and policies and measures promoting renewable energy usage. Reinforcement learning (RL) is an agent-based modelling technique that has proven successful in many applications, particularly in artificial intelligence. RL has attracted research attention owing to its utilization in building energy management (BEM) applications. In this work, the latest research advances that utilize this method are investigated and discussed, primarily its usage in modelling complex building energy problems, building energy consumption control, optimization for comfort and cost savings, and the enhancement of demand forecasting algorithms. Furthermore, the combination of RL with other deep learning methods is discussed. As a state-of-the-art technology in smart grid building applications, RL is applied for control purposes and forecasting enhancement.

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Analyzing the Impacts of a Deep-Learning Based Day-Ahead Residential Demand Response Model on The Jordanian Power Sector in Winter Season

Cited by 2 publications

References 12 publications

Estimating Ground Elevation using Borehole Information: A Case of Metro Manila, Philippines

Estimating Ground Elevation using Borehole Information: A Case of Metro Manila, Philippines

Recent Advances in Reinforcement Learning Applications for Building Energy Management: A Mini Review.

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