Sustainable Optimization for Innovative Practices of Smart Buildings with the AI Based Deep Learning Techniques | Chapter 3 | Research Updates in Mathematics and Computer Science Vol. 7

 

A major driver in the rethinking of sustainable engineering and management practices is artificial intelligence (AI), especially in the use of cutting-edge deep learning techniques. The primary focus of this study is to assess the efficacy of Long Short-Term Memory (LSTM) networks as an example of the substantial role that artificial intelligence (AI) plays. When it comes to improving operational performance, optimizing resource allocation, and reducing environmental implications, the Long Short-Term Memory (LSTM) model—a complex sort of recurrent neural network—is crucial. An interesting case study illustrating the use of LSTM algorithms to optimize smart building energy usage in real time is included in this research. By utilizing LSTM for comprehensive pattern analysis and making real-time adjustments, AI exhibits impressive efficiency gains in reducing energy waste. Within the broader context of sustainable engineering, this study demonstrates the effectiveness and efficiency of Long Short-Term Memory (LSTM), therefore contributing significantly to the development of a resilient and ecologically conscious future.

 

Author(s) Details

 

K. Rajendra Prasad

Department of CSE (CS), Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/rumcs/v7/452

A Comprehensive Review of Advanced AI/ML Applications in Solar Radiation Prediction | Chapter 9 | Scientific Research, New Technologies and Applications Vol. 7

Recent advancements in AI/ML have focused on improving the robustness of solar radiation predictions through hybrid models, combining machine learning algorithms with traditional physical models, or blending multiple ML techniques. Solar energy is one of the most abundant renewable energy sources available globally, and accurate solar radiation prediction is crucial for efficient energy management, grid optimization, and the development of solar technologies. The advent of machine learning and artificial intelligence has revolutionized the field of solar radiation forecasting, offering more accurate, scalable, and adaptive models. In India, using AI/ML for solar radiation prediction is challenged by inconsistent data quality due to limited, fragmented measurement stations across diverse climates. Large-scale, high-quality datasets are often unavailable, especially in remote or rural areas. The high computational resource requirements further strain infrastructure, particularly in less developed regions. Additionally, interpreting AI/ML models in this context can be difficult, limiting trust in predictions and wider adoption across the country. This review provides a comprehensive overview of the state-of-the-art ML and AI approaches used for solar radiation prediction, including traditional models such as regression and time-series analysis, as well as advanced techniques like deep learning and ensemble models. Additionally, the article discusses the challenges, limitations, and future directions in this field. The future of ML and AI in solar radiation prediction looks promising, with potential advancements in quantum computing and the integration of edge computing for real-time forecasting.

 

Author (s) Details

 

R. Meenal
V.S.B Engineering College, Karur - 639111, TN, India.

 

R. Devprakash
Lincoln University College, Petaling Jaya, 47301, Malaysia.

 

E. Rajasekaran
V.S.B Engineering College, Karur - 639111, TN, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/srnta/v7/2935

Sustainable Optimization for Innovative Practices of Smart Buildings with the AI Based Deep Learning Techniques | Chapter 4 | Research Updates in Mathematics and Computer Science Vol. 7

 

A major driver in the rethinking of sustainable engineering and management practices is artificial intelligence (AI), especially in the use of cutting-edge deep learning techniques. The primary focus of this study is to assess the efficacy of Long Short-Term Memory (LSTM) networks as an example of the substantial role that artificial intelligence (AI) plays. When it comes to improving operational performance, optimizing resource allocation, and reducing environmental implications, the Long Short-Term Memory (LSTM) model—a complex sort of recurrent neural network—is crucial. An interesting case study illustrating the use of LSTM algorithms to optimize smart building energy usage in real time is included in this research. By utilizing LSTM for comprehensive pattern analysis and making real-time adjustments, AI exhibits impressive efficiency gains in reducing energy waste. Within the broader context of sustainable engineering, this study demonstrates the effectiveness and efficiency of Long Short-Term Memory (LSTM), therefore contributing significantly to the development of a resilient and ecologically conscious future.

 

Author(s)details:-

 

K. Rajendra Prasad (Professor)
Department of CSE (CS), Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.

 

Please See the book here :- https://doi.org/10.9734/bpi/rumcs/v7/452