Developing of an Offline Monitoring Method for the Energy Demand of a Healthcare Facility in Italy

Zini, Marco; Carcasci, Carlo

doi:10.13044/j.sdewes.d10.0421

Cited by 6 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If n = 1, we talk about LR models, otherwise, if n >1 the above-mentioned equation is called Multiple Linear Regression (MLR) [15]. MLR models have been used for energy consumption predicting in a massive way since the last years, with good results and they are accepted methods for energy consumption evaluation and monitoring, for their low complexity and computational cost [10], [16], [17].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…The CUSUM (CUmulative SUM) method consists of calculating the differences between the real consumption and the predicted one and using them to generate the control chart [10]. The following equation shows how the CUSUM is calculated for the dataset:…”

Section: Cusummentioning

confidence: 99%

“…This work attempts to fill this knowledge gap and its aim is to develop a methodology of a CGS dataset analysis and features selection to deploy a machine learning model that will serve as a tool for monitoring tasks of the performance of the CGS preheating energy consumption. Machine Learning (ML)-based methods for monitoring and evaluating energy consumption performance are now widely used in many sectors, from civil buildings [9], [10], [11], industrial plants [12], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…Model application: CuSum ChartFigure6shows the trend of the CuSum calculated using equation(10) for the entire dataset with which the model was trained and over the following two months on which the performance of the CGS is tested. The two dashed red lines are the lower and upper control bands obtained by calculating the standard deviation of the CuSum over the training period (blue) and multiplying it by a safety coefficient of 3 as proposed in[20].…”

mentioning

confidence: 99%

See 3 more Smart Citations

Data-driven modelling for gas consumption prediction at City Gate Stations

Cheli

Meazzini²,

Busi³

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

City Gate Stations (CGS) are critical elements of Natural Gas (NG) distribution systems, as they connect national high-pressure transmission networks with local low-pressure networks. One of their main tasks is to pre-heat the gas to avoid dangerous sub-cooling due to the Joule-Thompson effect after the pressure reduction stage. For this process, significant amounts of thermal energy are required, usually obtained by burning part of the gas flow rate. This work aims to develop a data-driven model that will serve as a tool to predict and monitor the thermal consumption of the CGS. The plant chosen as a case study for this activity is in a region of central Italy. A Multiple Linear Regression (MLR) model is developed and trained, and its predictive performance is evaluated. The model results achieved an accuracy of over 95% for the coefficient of determination. The method makes it possible to create a baseline consumption model and evaluate the performance of the CGS over time using the CUSUM technique to find variations between actual and modelled gas consumption, being an essential tool for monitoring the effectiveness of the NG preheating system.

show abstract

Section: Machine Learning Modelsmentioning

confidence: 99%

Section: Cusummentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Data-driven modelling for gas consumption prediction at City Gate Stations

Cheli

Meazzini²,

Busi³

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…The result/output is denoted by C˙t called as input activation vector. Next, we multiply the output of tanh with the output of the sigmoid function [45]. The decision to either keep or discard the data will be taken by the sigmoid output equation of the input gate given in Equation 4, as it.…”

Section: Input Gatementioning

confidence: 99%

Prediction of Electric Power Demand of HVACs for Operating Rooms in Case of Dynamic Set Points of Temperature: A Case Study

Ullah

Ahmad

Yasin

et al. 2022

JCBI

View full text Add to dashboard Cite

In healthcare settings, particularly in areas such as operating rooms and intensive care units, there is a need for a dynamically controlled temperature environment that can adapt to the changing needs of both patients and healthcare workers. This is due to the fact that the desired temperature can vary depending on the condition of the patient and the specific requirements of surgical and treatment procedures. To address this need, our objective is to develop a tool for predicting the electric power needed to maintain a desired temperature in these critical care areas. Previous research has employed artificial learning algorithms and mathematical equations to predict electric power for various types and sizes of buildings, with promising results. However, our study focuses specifically on critical care areas within hospitals and utilizes fluctuating temperature set-points to predict power demand using historical weather data and Building Management System (BMS) data. We employed both Multi-Layer Artificial Neural Network (ML-ANN) and Long short-term memory (LSTM) models for this purpose and found that ML-ANN outperformed LSTM. The results showed that the ML-ANN model performed better than the LSTM model, with a testing accuracy of 96% compared to 78% for the LSTM model. This indicates that the ML-ANN model was more accurate in predicting the power consumption for the desired temperature in the operating room.

show abstract