The Technological Assessment of Green Buildings using Artificial Neural Networks

Heliyon, Journal Year: 2024, Volume and Issue: 10(16), P. e36400 - e36400

Published: Aug. 1, 2024

This study aims to construct a comprehensive evaluation model for efficiently assessing appropriate technologies within green buildings. Initially, an Internet of Things (IoT)-based environmental monitoring system is devised and implemented collect real-time parameters both inside outside the building. To evaluate technical suitability buildings, this employs multifaceted approach encompassing various criteria, including energy efficiency, impact, economic benefits, user comfort, sustainability. Specifically, it involves parameters, analysis consumption data, indoor quality indicators derived from satisfaction surveys. Subsequently, Multi-Layer Perceptron (MLP) selected as conventional artificial neural network (ANN) model, while Long Short-Term Memory (LSTM) chosen advanced recurrent in realm deep learning. These models are utilized process explore collected data assess The dataset comprises physical quantities such temperature, humidity, light intensity, well efficiency building operating costs. Furthermore, assessment considers building's life cycle factors health, safety. By incorporating these holistic achieved, ensuring technologies' effectiveness. prediction results demonstrate that proposed hybrid exhibits high accuracy robust stability predicting parameters. For instance, Root Mean Square Error (RMSE) temperature 1.2 °C, Absolute (MAE) 0.9 determination coefficient (R

Language: Английский

Behavior of LSTM and Transformer Deep Learning Models in Flood Simulation Considering South Asian Tropical Climate

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: March 18, 2024

Abstract The imperative for a reliable and accurate flood forecasting procedure stem from the hazardous nature of disaster. In response, researchers are increasingly turning to innovative approaches, particularly machine learning models, which offer enhanced accuracy compared traditional methods. However, notable gap exists in literature concerning studies focused on South Asian tropical region, possesses distinct climate characteristics. This study investigates applicability behavior Long Short-Term Memory (LSTM) Transformer models simulation with one day lead time, at lower reach Mahaweli catchment Sri Lanka, is mostly affected by Northeast Monsoon. importance different input variables prediction was also key focus this study. Input features included observed rainfall data collected three nearby rain gauges, as well historical discharge target river gauge. Results showed that use past water level denotes higher impact output other such rainfall, both architectures. All denoted satisfactory performances simulating daily levels, especially low stream flows, Nash Sutcliffe Efficiency (NSE) values greater than 0.77 while Encoder model superior performance Decoder models.

Language: Английский

Lake Water Level Forecasting Using LSTM and GRU: A Deep Learning Approach

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 197 - 216

Published: Jan. 1, 2024

Language: Английский