scholarly journals Investigation and prediction of Energy consumption at St. Olavs Hospital

2021 ◽  
Vol 246 ◽  
pp. 04003
Author(s):  
Kristofersen, by Hans Smedsrud ◽  
Kai Xue ◽  
Zhirong Yang ◽  
Liv-Inger Stenstad ◽  
Tor Emil Giske ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Operating Room ◽  
Energy Use ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Heating And Cooling ◽  
Margin Of Error ◽  
The Impact ◽  
Operational Data

The objective of this study is to evaluate and predict the energy use in different buildings during COVID-19 pandemic period at St. Olavs Hospital in Trondheim. Based on machine learning, operational data from St. Olavs hospital combined with weather data will be used to predict energy use for the hospital. Analysis of the energy data showed that the case buildings at the hospital did not have any different energy use during the pandemic this year compared to the same period last year, except for the lab center. The energy consumption of electricity, heating and cooling is very similar both in 2019 and 2020 for all buildings, but in 2020 during the pandemic, the lab center had a reduction of 35% in electricity, compared to last year. An analysis of the energy needed for heating and cooling in the end of June to the end of November was also calculated for operating room 1 and was estimated to 256 kWh/m2 for operation room 1. The machine learning algorithms perform very well to predict the energy consumption of case buildings, Random Forest and AdaBoost proves as the best models, with less than 10% margin of error, some of the models have only 4% error. An analysis of the effect of humidification of ventilation air on energy consumption in operating room 1 was also carried out. The impact on energy consumption were high in winter and will at the coldest periods be able to double the energy consumption needed in the ventilation.

scholarly journals Forecasting residential gas consumption with machine learning algorithms on weather data

2019 ◽  
Vol 111 ◽  
pp. 05019
Author(s):  
Brian de Keijzer ◽  
Pol de Visser ◽  
Víctor García Romillo ◽  
Víctor Gómez Muñoz ◽  
Daan Boesten ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Energy Use ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Computational Time ◽  
Percentage Error ◽  
Learning Models ◽  
Gas Consumption ◽  
Machine Learning Models

Machine learning models have proven to be reliable methods in the forecasting of energy use in commercial and office buildings. However, little research has been done on energy forecasting in dwellings, mainly due to the difficulty of obtaining household level data while keeping the privacy of inhabitants in mind. Gaining insight into the energy consumption in the near future can be helpful in balancing the grid and insights in how to reduce the energy consumption can be received. In collaboration with OPSCHALER, a measurement campaign on the influence of housing characteristics on energy costs and comfort, several machine learning models were compared on forecasting performance and the computational time needed. Nine months of data containing the mean gas consumption of 52 dwellings on a one hour resolution was used for this research. The first 6 months were used for training, whereas the last 3 months were used to evaluate the models. The results showed that the Deep Neural Network (DNN) performed best with a 50.1 % Mean Absolute Percentage Error (MAPE) on a one hour resolution. When comparing daily and weekly resolutions, the Multivariate Linear Regression (MVLR) outperformed other models, with a 20.1 % and 17.0 % MAPE, respectively. The models were programmed in Python.

scholarly journals A Complementary Approach to Traditional Energy Balances for Assessing Energy Efficiency Measures in Final Uses: The Case of Space Heating and Cooling in Argentina

2020 ◽  
Vol 12 (16) ◽  
pp. 6563
Author(s):  
Roque G Stagnitta ◽  
Matteo V Rocco ◽  
Emanuela Colombo
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Primary Energy ◽  
Embodied Energy ◽  
Space Heating ◽  
Output Analysis ◽  
Final Energy ◽  
Heating And Cooling ◽  
Energy Balances ◽  
The Impact

Energy balances have been historically conceived based on a supply-side perspective, providing neither detailed information about energy conversion into useful services nor the effects that may be induced by the application of policies in other sectors to energy consumption. This article proposes an approach to a thorough assessment of the impact of efficiency policies on final energy uses, focusing on residential space heating and cooling, and capable of: (1) quantifying final useful services provided and (2) accounting for the global impact of efficiency policies on final energy use, taking advantage of Input–Output analysis. This approach is applied in five cities of Argentina. Firstly, the quantity of energy service provided (i.e., level of thermal comfort) for each city is evaluated and compared with the defined target. It is found out that heating comfort is guaranteed approximately as established, whereas in the cooling case the provision is twice the established level. Secondly, primary energy consumption of heating and cooling services is evaluated before and after different efficiency improvement policies. The results show that the major primary energy saving (52%) is obtained from the upgrading appliances scenario and reflect the importance of accounting for embodied energy in goods and services involved in interventions.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Prediction of aircraft estimated time of arrival using machine learning methods

2021 ◽  
pp. 1-15
Author(s):  
O. Basturk ◽  
C. Cetek
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Time Of Arrival ◽  
Learning Models ◽  
Trajectory Data ◽  
Different Sources ◽  
Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

scholarly journals Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3876
Author(s):  
Sameh Monna ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
Aiman Albatayneh ◽  
Patrick Dutournie ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Energy Savings ◽  
Residential Buildings ◽  
Residential Building ◽  
Simulation Models ◽  
Water Heating ◽  
Heating And Cooling ◽  
Space Cooling ◽  
Cooling Loads

Since buildings are one of the major contributors to global warming, efforts should be intensified to make them more energy-efficient, particularly existing buildings. This research intends to analyze the energy savings from a suggested retrofitting program using energy simulation for typical existing residential buildings. For the assessment of the energy retrofitting program using computer simulation, the most commonly utilized residential building types were selected. The energy consumption of those selected residential buildings was assessed, and a baseline for evaluating energy retrofitting was established. Three levels of retrofitting programs were implemented. These levels were ordered by cost, with the first level being the least costly and the third level is the most expensive. The simulation models were created for two different types of buildings in three different climatic zones in Palestine. The findings suggest that water heating, space heating, space cooling, and electric lighting are the highest energy consumers in ordinary houses. Level one measures resulted in a 19–24 percent decrease in energy consumption due to reduced heating and cooling loads. The use of a combination of levels one and two resulted in a decrease of energy consumption for heating, cooling, and lighting by 50–57%. The use of the three levels resulted in a decrease of 71–80% in total energy usage for heating, cooling, lighting, water heating, and air conditioning.

scholarly journals Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

2021 ◽  
Vol 13 (4) ◽  
pp. 1595
Author(s):  
Valeria Todeschi ◽  
Roberto Boghetti ◽  
Jérôme H. Kämpf ◽  
Guglielmina Mutani
Keyword(s):  
Machine Learning ◽  
Energy Use ◽  
Residential Buildings ◽  
Building Energy ◽  
Energy Performance ◽  
Space Heating ◽  
Engineering Model ◽  
Building Energy Use ◽  
The Impact ◽  
The City

Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

scholarly journals Modeling Mine Workforce Fatigue: Finding Leading Indicators of Fatigue in Operational Data Sets

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 621
Author(s):  
Elaheh Talebi ◽  
W. Pratt Rogers ◽  
Tyler Morgan ◽  
Frank A. Drews
Keyword(s):  
Health And Safety ◽  
Time Of Day ◽  
Weather Data ◽  
Data Sets ◽  
Leading Indicators ◽  
Safety Control ◽  
Model Complex ◽  
Fatigue Monitoring ◽  
The Impact ◽  
Operational Data

Mine workers operate heavy equipment while experiencing varying psychological and physiological impacts caused by fatigue. These impacts vary in scope and severity across operators and unique mine operations. Previous studies show the impact of fatigue on individuals, raising substantial concerns about the safety of operation. Unfortunately, while data exist to illustrate the risks, the mechanisms and complex pattern of contributors to fatigue are not understood sufficiently, illustrating the need for new methods to model and manage the severity of fatigue’s impact on performance and safety. Modern technology and computational intelligence can provide tools to improve practitioners’ understanding of workforce fatigue. Many mines have invested in fatigue monitoring technology (PERCLOS, EEG caps, etc.) as a part of their health and safety control system. Unfortunately, these systems provide “lagging indicators” of fatigue and, in many instances, only provide fatigue alerts too late in the worker fatigue cycle. Thus, the following question arises: can other operational technology systems provide leading indicators that managers and front-line supervisors can use to help their operators to cope with fatigue levels? This paper explores common data sets available at most modern mines and how these operational data sets can be used to model fatigue. The available data sets include operational, health and safety, equipment health, fatigue monitoring and weather data. A machine learning (ML) algorithm is presented as a tool to process and model complex issues such as fatigue. Thus, ML is used in this study to identify potential leading indicators that can help management to make better decisions. Initial findings confirm existing knowledge tying fatigue to time of day and hours worked. These are the first generation of models and future models will be forthcoming.

An Investigation of the Impact of COVID-19 Pandemic on Energy Consumption in the United States

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Lindsey Kahn ◽  
Hamidreza Najafi
Keyword(s):  
Energy Consumption ◽  
Correlation Coefficient ◽  
Energy Use ◽  
The United States ◽  
Annual Growth Rate ◽  
Total Consumption ◽  
The Usa ◽  
End Use ◽  
The Times ◽  
The Impact

Abstract Lockdown measures and mobility restrictions to combat the spread of COVID-19 have impacted energy consumption patterns. The overall decline of energy use during lockdown restrictions can best be identified through the analysis of energy consumption by source and end-use sectors. Using monthly energy consumption data, the total 9-months use between January and September for the years 2015–2020 is calculated for each end-use sector (transportation, industrial, residential, and commercial). The cumulative consumption within these 9 months of the petroleum, natural gas, biomass, and electricity energy by the various end-use sectors are compared. The analysis shows that the transportation sector experienced the greatest decline (14.38%). To further analyze the impact of COVID-19 on each state within the USA, the consumption of electricity by each state and each end-use sector in the times before and during the pandemic is used to identify the impact of specific lockdown procedures on energy use. The distinction of state-by-state analysis in this study provides a unique metric for consumption forecasting. The average total consumption for each state was found for the years 2015–2019. The total average annual growth rate (AAGR) for 2020 was used to find a correlation coefficient between COVID-19 case and death rate, population density, and lockdown duration. A correlation coefficient was also calculated between the 2020 AAGR for all sectors and AAGR for each individual end-user. The results show that Indiana had the highest percent reduction in consumption of 10.07% while North Dakota had the highest consumption increase of 7.61%. This is likely due to the amount of industrial consumption relative to other sectors in the state.

People or Systems: Does Productivity Enhancement Matter More than Energy Management in LEED Certified Buildings?

2021 ◽  
Vol 13 (24) ◽  
pp. 13863
Author(s):  
Yana Akhtyrska ◽  
Franz Fuerst
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Energy Use ◽  
Management Practices ◽  
Energy Savings ◽  
Occupant Comfort ◽  
Energy Use Intensity ◽  
Using Data ◽  
Advanced Metering ◽  
The Impact

This study examines the impact of energy management and productivity-enhancing measures, implemented as part of LEED Existing Buildings Operations and Management (EBOM) certification, on source energy use intensity and rental premiums of office spaces using data on four major US markets. Energy management practices, comprised of commissioning and advanced metering, may reduce energy usage. Conversely, improving air quality and occupant comfort in an effort to increase worker productivity may in turn lead to higher overall energy consumption. The willingness to pay for these features in rental office buildings is hypothesised to depend not only on the extent to which productivity gains enhance the profits of a commercial tenant but also on the lease arrangements for passing any energy savings to the tenant. We apply a difference-in-differences method at a LEED EBOM certification group level and a multi-level modelling approach with a panel data structure. The results indicate that energy management and indoor environment practices have the expected effect on energy consumption as described above. However, the magnitude of the achieved rental premiums appears to be independent of the lease type.

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