scholarly journals Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5556
Author(s):  
Benedetto Grillone ◽  
Gerard Mor ◽  
Stoyan Danov ◽  
Jordi Cipriano ◽  
Florencia Lazzari ◽  
...  
Keyword(s):  
Linear Regression ◽  
Energy Use ◽  
Residential Buildings ◽  
Data Driven ◽  
Multilevel Regression ◽  
Measurement And Verification ◽  
Bayesian Linear Regression ◽  
Bayesian Methodologies ◽  
Posterior Estimation

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities.

scholarly journals Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2055 ◽  
Author(s):  
Francesco Mancini ◽  
Gianluigi Lo Basso ◽  
Livio De Santoli
Keyword(s):  
Data Analysis ◽  
Energy Use ◽  
Residential Buildings ◽  
Automation System ◽  
Research Activity ◽  
Flexible Loads ◽  
On Line ◽  
House Floor ◽  
Statistical Sample

This work shows the outcomes of a research activity aimed at the energy characterization of residential users. Specifically, by data analysis related to the real energy consumption of sample buildings, the flexible loads amount has been identified so as to investigate on the opportunity to implement a demand/response (DR) program. The most meaningful input data have been collected by an on-line questionnaire created within an Excel spreadsheet allowing one to simulate and compare the calculations with the actual dwellings’ consumption; 412 questionnaires have been used as statistical sample and simulations have been performed based on single-zone dynamic model. Additionally, once the energy consumptions have been sorted by the different services, reference key performance indicators (KPIs) have been also calculated normalising those ones by people and house floor surface. From data analysis, it emerges how the Italian residential users are not very electrified. Furthermore, the flexible loads are low and, implementing minor maintenance interventions, the potential of flexibility can decrease up to 20%. For that reason, the current research can be further developed by investigating on suitable flexibility extensions as well as on the automation system requirements which is needed managing the flexible loads.

Data driven occupancy information for energy simulation and energy use assessment in residential buildings

Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 119539
Author(s):  
Karthik Panchabikesan ◽  
Fariborz Haghighat ◽  
Mohamed El Mankibi
Keyword(s):  
Energy Use ◽  
Residential Buildings ◽  
Energy Simulation ◽  
Data Driven

Evaluation of Measurement and Verification Procedures for Retrofit Savings Using Calibrated Energy Building Models

2010 ◽  
Author(s):  
Niko Kalinic ◽  
Moncef Krarti
Keyword(s):  
Energy Conservation ◽  
Energy Use ◽  
Energy Savings ◽  
Residential Buildings ◽  
Simulation Models ◽  
Building Envelope ◽  
Measurement And Verification ◽  
Conservation Measures ◽  
Before And After ◽  
Energy Conservation Measures

Calibrated energy simulations are often used to predict savings from energy conservation measures with little information about their associated prediction uncertainties. In this paper, the savings predicted by calibrated simulation models are compared to actual savings obtained through monitoring energy use before and after implementing selected energy conservation measures for three residential buildings. Both building envelope and HVAC system related energy conservation measures are considered in the study. Through case studies, this validity of using calibrated energy models for the estimation and verification of savings associated with energy conservation measures is thoroughly evaluated. Moreover, the paper provides useful guidelines for using calibrated models for measurement and verification energy savings from various weatherization programs specific to residential buildings.

scholarly journals 222 Partitioning of retained energy between protein and fat gain, and heat of product formation and support metabolism in growing beef cattle

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 158-158
Author(s):  
Phillip A Lancaster
Keyword(s):  
Linear Regression ◽  
Mixed Models ◽  
Multivariate Regression ◽  
Literature Search ◽  
Energy Use ◽  
Random Variable ◽  
Product Formation ◽  
Metabolizable Energy ◽  
Overall Efficiency ◽  
Metabolizable Energy Intake

Abstract Multiple linear regression inaccurately computes the efficiency of energy use for protein and fat gain. The objective was to quantify efficiency of metabolizable energy use for protein and fat gain along with heats of product formation and support metabolism. A literature search was performed to compile data (31 studies, 214 treatment means) on metabolizable energy intake (MEI) and composition of empty body gain in growing steers and heifers. Data analyses were performed using R statistical package for mixed models with study as random variable. Linear regression of MEI on energy gain (EG; P < 0.001; R2 = 0.627) resulted in an estimate of metabolizable energy for maintenance (MEm) of 156 kcal/kg.75 and efficiency of ME use for gain of 0.518. Linear regression of MEI on EG as protein and fat (P < 0.001; R2 = 0.623) resulted in an estimate of MEm of 149 kcal/kg.75, and efficiency of protein (kp) and fat (kf) gain of 0.274 and 0.585, respectively, resulting in an overall efficiency of EG of 0.520. Nonlinear regression model (EG = kg*(MEI-MEm)) resulted in an estimate of MEm of 103 kcal/kg.75 and efficiency of EG of 0.342. The heat of product formation was assumed to be 0.48 (1 – 0.52) and the heat of support metabolism (HiEv) 0.18 (0.52 – 0.34). Multivariate regression was used to fit simultaneous models for EG as protein (EGp = (kp+HiEvp)*k*MEA) and fat (EGf = (kf+(0.18-HiEvp))*(1-k)*MEA). Estimates (P < 0.001) of kp and kf were 0.12 ± 0.01 and 0.63 ± 0.02, and HiEvp and proportion of ME available for protein gain (k) were 0.11 ± 0.01 and 0.75 ± 0.01, respectively. The heat of product formation and support metabolism for protein were 0.77 and 0.11, and fat were 0.30 and 0.07, respectively. In conclusion, efficiency of ME use for protein was lesser than for fat gain, and heat of support metabolism was greater for protein than fat gain.

scholarly journals Overheating Risk and Energy Demand of Nordic Old and New Apartment Buildings during Average and Extreme Weather Conditions under a Changing Climate

2021 ◽  
Vol 11 (9) ◽  
pp. 3972
Author(s):  
Azin Velashjerdi Farahani ◽  
Juha Jokisalo ◽  
Natalia Korhonen ◽  
Kirsti Jylhä ◽  
Kimmo Ruosteenoja ◽  
...  
Keyword(s):  
Climate Change ◽  
Energy Demand ◽  
Energy Use ◽  
Residential Buildings ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Living Room ◽  
Extra Energy ◽  
Cooling Unit ◽  
Indoor Conditions

The global average air temperature is increasing as a manifestation of climate change and more intense and frequent heatwaves are expected to be associated with this rise worldwide, including northern Europe. Summertime indoor conditions in residential buildings and the health of occupants are influenced by climate change, particularly if no mechanical cooling is used. The energy use of buildings contributes to climate change through greenhouse gas emissions. It is, therefore, necessary to analyze the effects of climate change on the overheating risk and energy demand of residential buildings and to assess the efficiency of various measures to alleviate the overheating. In this study, simulations of dynamic energy and indoor conditions in a new and an old apartment building are performed using two climate scenarios for southern Finland, one for average and the other for extreme weather conditions in 2050. The evaluated measures against overheating included orientations, blinds, site shading, window properties, openable windows, the split cooling unit, and the ventilation cooling and ventilation boost. In both buildings, the overheating risk is high in the current and projected future average climate and, in particular, during exceptionally hot summers. The indoor conditions are occasionally even injurious for the health of occupants. The openable windows and ventilation cooling with ventilation boost were effective in improving the indoor conditions, during both current and future average and extreme weather conditions. However, the split cooling unit installed in the living room was the only studied solution able to completely prevent overheating in all the spaces with a fairly small amount of extra energy usage.

scholarly journals Optimal Control Strategies for Switchable Transparent Insulation Systems Applied to Smart Windows for US Residential Buildings

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2917
Author(s):  
Mohammad Dabbagh ◽  
Moncef Krarti
Keyword(s):  
Energy Use ◽  
Control Strategies ◽  
Residential Buildings ◽  
Residential Building ◽  
Optimization Approach ◽  
Optimal Controls ◽  
Peak Demand ◽  
Smart Windows ◽  
Insulation Systems ◽  
Rule Set

This paper evaluates the potential energy use and peak demand savings associated with optimal controls of switchable transparent insulation systems (STIS) applied to smart windows for US residential buildings. The optimal controls are developed based on Genetic Algorithm (GA) to identify the automatic settings of the dynamic shades. First, switchable insulation systems and their operation mechanisms are briefly described when combined with smart windows. Then, the GA-based optimization approach is outlined to operate switchable insulation systems applied to windows for a prototypical US residential building. The optimized controls are implemented to reduce heating and cooling energy end-uses for a house located four US locations, during three representative days of swing, summer, and winter seasons. The performance of optimal controller is compared to that obtained using simplified rule-based control sets to operate the dynamic insulation systems. The analysis results indicate that optimized controls of STISs can save up to 81.8% in daily thermal loads compared to the simplified rule-set especially when dwellings are located in hot climates such as that of Phoenix, AZ. Moreover, optimally controlled STISs can reduce electrical peak demand by up to 49.8% compared to the simplified rule-set, indicating significant energy efficiency and demand response potentials of the SIS technology when applied to US residential buildings.

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