Multivariate Regression Modeling

1998 ◽  
Vol 120 (3) ◽  
pp. 177-184 ◽  
Author(s):  
S. Katipamula ◽  
T. A. Reddy ◽  
D. E. Claridge
Keyword(s):  
Energy Consumption ◽  
Regression Models ◽  
Energy Use ◽  
Complex Function ◽  
Climatic Conditions ◽  
Regression Modeling ◽  
Commercial Buildings ◽  
Coefficient Of Determination ◽  
Single Variable ◽  
Variable Model

An empirical or regression modeling approach is simple to develop and easy to use compared to detailed hourly simulations of energy use in commercial buildings. Therefore, regression models developed from measured energy data are becoming an increasingly popular method for determining retrofit savings or identifying operational and maintenance (O&M) problems. Because energy consumption in large commercial buildings is a complex function of climatic conditions, building characteristics, building usage, system characteristics and type of heating, ventilation, and air conditioning (HVAC) equipment used, a multiple linear regression (MLR) model provides better accuracy than a single-variable model for modeling energy consumption. Also, when hourly monitored data are available, an issue which arises is what time resolution to adopt for regression models to be most accurate. This paper addresses both these topics. This paper reviews the literature on MLR models of building energy use, describes the methodology to develop MLR models, and highlights the usefulness of MLR models as baseline models and in detecting deviations in energy consumption resulting from major operational changes. The paper first develops the functional basis of cooling energy use for two commonly used HVAC systems: dual-duct constant volume (DDCV) and dual-duct variable air volume (DDVAV). Using these functional forms, the cooling energy consumption in five large commercial buildings located in central Texas were modeled at monthly, daily, hourly, and hour-of-day (HOD) time scales. Compared to the single-variable model (two-parameter model with outdoor dry-bulb as the only variable), MLR models showed a decrease in coefficient of variation (CV) between 10 percent to 60 percent, with an average decrease of about 33 percent, thus clearly indicating the superiority of MLR models. Although the models at the monthly time scale had higher coefficient of determination (R2) and lower CV than daily, hourly, and HOD models, the daily and HOD models proved more accurate at predicting cooling energy use.

scholarly journals Neural Methods Comparison for Prediction of Heating Energy Based on Few Hundreds Enhanced Buildings in Four Season’s Climate

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5453 ◽  
Author(s):  
Tomasz Szul ◽  
Krzysztof Nęcka ◽  
Thomas G. Mathia
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Residential Building ◽  
Regression Trees ◽  
Predictive Modelling ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
New Paradigm

Sustainable development and the increasing demand for equitable energy use as well as the reduction of waste of energy are the author’s social and scientific motivations. This new paradigm is the selection of a pertinent methodology to evaluate the efficiency of habitat thermomodernization, which is one of the scientific tasks of the presented study. In order to meet the social and scientific requirements, 380 buildings from the end of the last century (made of large plate technology), which were thermally improved at the beginning of the XXI century, were designed for a comparative analysis of the predictive modelling of heating energy consumption. A specific set of important variables characterizing the examined buildings has been identified. Groups of variables were used to estimate the energy consumption in such a way as to achieve a compromise between the difficulty of obtaining them and the quality of forecast. To predict energy consumption, the six most appropriate neural methods were used: artificial neural networks (ANN), general regression trees (CART), exhaustive regression trees (CHAID), support regression trees (SRT), support vectors (SV), and method multivariant adaptive regression splines (MARS). The quality assessment of the developed models used the mean absolute percentage error (MAPE) also known as mean absolute percentage deviation (MAPD), as well as mean bias error (MBE), coefficient of variance of the root mean square error (CV RMSE) and coefficient of determination (R2), which are accepted as statistical calibration standards by (American Society of Heating, Refrigerating and Air-Conditioning Engineers) ASHRAE. On this basis, the most effective method has been chosen, which gives the best results and therefore allows to forecast with great precision the energy consumption (after thermal improvement) for this type of residential building.

scholarly journals Estimation of Direct and Indirect Household CO2 Emissions in 49 Japanese Cities with Consideration of Regional Conditions

2020 ◽  
Vol 12 (11) ◽  
pp. 4678 ◽  
Author(s):  
Yujiro Hirano ◽  
Tomohiko Ihara ◽  
Masayuki Hara ◽  
Keita Honjo
Keyword(s):  
Energy Consumption ◽  
Co2 Emissions ◽  
Energy Use ◽  
Energy Savings ◽  
Climatic Conditions ◽  
Cold Regions ◽  
Large Cities ◽  
Direct Energy ◽  
The Relationship

We conducted a detailed estimation of direct and indirect CO2 emissions related to multi-person households in 49 Japanese cities. Direct energy consumption was decomposed into energy use in order to consider the relationship with regional conditions. The results showed that CO2 emissions from direct energy consumption were almost as large as indirect CO2 emissions induced by consuming products and services, suggesting that lifestyle improvements are important for both energy savings and reducing CO2 emissions relating to product and service consumption. In addition, CO2 emissions from direct energy consumption varied widely between cities, making them susceptible to regional conditions. We also calculated CO2 emissions from direct energy consumption and examined the regional conditions for individual forms of energy use. CO2 emissions were higher in cold regions and lower in larger cities. In Japan, large cities are often located in relatively warm areas, so we conducted an analysis to distinguish the effects of climatic conditions from those of urbanization. This analysis allowed us to clarify the effects of regional conditions on factors such as heating/cooling and the ratio of detached houses to apartments.

scholarly journals Application of DMAIC to improve energy consumption in a commercial building

2021 ◽  
Author(s):  
◽  
Kanyinda Kabuya
Keyword(s):  
Energy Consumption ◽  
Six Sigma ◽  
Energy Use ◽  
Electrical Energy ◽  
Commercial Buildings ◽  
Commercial Building ◽  
Total Consumption ◽  
Pareto Chart ◽  
Electrical Consumption

Improving energy use in a commercial building has become the subject of great importance in organizations worldwide. Improving energy usage refers to the efforts to reduce energy consumption. Reducing energy consumption in commercial buildings can be accomplished through continuous supervision using appropriate managerial techniques. Commercial companies are required to use energy more efficiently and participate in energy improvement. This study seeks to improve electrical energy consumption in commercial buildings by Analysing the electrical data consumption and identifying the factors that contribute to high consumption using Six Sigma DMAIC (Define-Measure- Analyse-Improve-Control) problem solving methodology. A case study was used to validate the DMAIC framework. Two years of electrical consumption data of a case study done from January 2018 to December 2019 was collected and analysed. The study revealed an average increment in energy consumption of 3.9 %. The outcomes using statistical Pareto chart showed that the boiler is the highest significant energy user in the building with 38.3% due; followed by the kitchen with 24.2 %, followed by DB A and lifts with 20,1 % and the rest with 17.37 %. After the campaign of DMAIC, there was a reduction of 6 % in boiler consumption which was 2.3 % reduction of total consumption of the month for the building. Therefore, the study successfully demonstrates how Six Sigma DMAIC methodology can be applied to improve electrical consumption in a commercial building and reduce its related costs.

scholarly journals ANALYSIS OF RESULTS OF MEASURES PERFORMED TO INCREASE ENERGY EFFICIENCY IN VASIL KARAGIOZOV HIGH SCHOOL OF YAMBOL, BULGARIA

2018 ◽  
Vol 6 (4) ◽  
pp. 306-310 ◽  
Author(s):  
Ivan Binev
Keyword(s):  
High School ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Saving ◽  
Energy Use ◽  
Energy Savings ◽  
Climatic Conditions ◽  
Improve Energy Efficiency ◽  
Before And After ◽  
The Impact

The report analyzes the results of the implemented measures to improve energy efficiency in Vasil Karagiozov High school of Yambol, Bulgaria. Energy savings are determined by measuring and/or calculating energy consumption with previously adopted baseline levels, implementing a measure or program to improve energy efficiency by providing normalized corrections corresponding to the impact of specific climatic conditions on energy use. A reference heating energy consumption of 38.62 kWh/m2 was determined after the renovation of the building. Comparing the reference energy costs for heating before and after the implementation of the energy saving measures show a real decrease of the energy consumption for heating by 53.44%. Compared to the reference energy consumption for heating before and after the energy saving measures show an actual reduction of energy consumption for heating by 47.86%.

scholarly journals A Method for Fault Detection and Diagnostics in Ventilation Units Using Virtual Sensors

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3931 ◽  
Author(s):  
Claudio Mattera ◽  
Joseba Quevedo ◽  
Teresa Escobet ◽  
Hamid Shaker ◽  
Muhyiddine Jradi
Keyword(s):  
Energy Consumption ◽  
Linear Regression ◽  
Fault Detection ◽  
Regression Models ◽  
Fault Tolerant ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
Virtual Sensors ◽  
Correct Operation ◽  
Acceptable Range

Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings’ energy efficiency and occupancy comfort. In order to ensure their correct operation, proper fault detection and diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose exploiting physical relations inside ventilation units to create virtual sensors from other sensors’ readings, introducing redundancy in the system. We use two different measures to detect when a virtual sensor deviates from the physical one: coefficient of determination for linear models, and acceptable range. We tested our method on a real building at the University of Southern Denmark, developing three virtual sensors: temperature, airflow, and fan speed. We employed linear regression models, statistical models, and non-linear regression models. All models detected an anomalous strong oscillation in the temperature sensors. Readings fell outside the acceptable range and the coefficient of determination dropped. Our method showed promising results by introducing redundancy in the system, which can benefit several applications, such as fault detection and diagnostics and fault-tolerant control. Future work will be necessary to discover thresholds and set up automatic fault detection and diagnostics.

scholarly journals An improved ARX model for hourly cooling load prediction of office buildings in different climates

2018 ◽  
Vol 175 ◽  
pp. 03027 ◽  
Author(s):  
Chengliang Fan ◽  
Yundan Liao ◽  
Yunfei Ding
Keyword(s):  
Regression Analysis ◽  
Regression Models ◽  
Climatic Conditions ◽  
Office Buildings ◽  
Coefficient Of Determination ◽  
Cooling Load ◽  
Load Prediction ◽  
Piecewise Linearization ◽  
Arx Model ◽  
Analysis Methods

An attempt was made to develop an improved autoregressive with exogenous (ARX) model for office buildings cooling load prediction in five major climates of China. The cooling load prediction methods can be arranged into three categories: regression analysis, energy simulation, and artificial intelligence. Among them, the regression analysis methods using regression models are much simple and practical for real applications. However, traditional regression models are often helpless to manage multiparameter dynamic changes, making it not accurate as the other two categories. Many of the existing cooling load prediction studies use piecewise linearization to manage nonlinearity. To improve the prediction accuracy of regression analysis methods, higher order and interaction terms are included in improved ARX based on traditional ARX model. The improved ARX model consists of eight variables, with eleven coefficients accessed at a time. For applications and evaluations, an office building in major cities within each climatic zone was selected as a representation. These cities were Harbin, Beijing, Nanjing, Kunming and Guangzhou respectively. The coefficient of determination R2 is greater than 0.9 in five cities. The prediction results show that the improved ARX model can adapt to different climatic conditions, including those nonlinearity cases.

Uncertainty in Baseline Regression Modeling and in Determination of Retrofit Savings

1998 ◽  
Vol 120 (3) ◽  
pp. 185-192 ◽  
Author(s):  
T. A. Reddy ◽  
J. K. Kissock ◽  
D. K. Ruch
Keyword(s):  
Regression Models ◽  
Energy Use ◽  
Energy Savings ◽  
Climatic Conditions ◽  
Standard Type ◽  
Operating Modes ◽  
Constant Variance ◽  
Model Residuals ◽  
Range Of Variation

The objective of this paper is to discuss the various sources of uncertainty inherent in the estimation of actual measured energy savings from baseline regression models, and to present pertinent statistical concepts and formulae to determine this uncertainty. Regression models of energy use in commercial buildings are not of the “standard” type addressed in textbooks because of the changepoint behavior of the models and the effect of patterned and non-constant variance residuals (largely as a result of changes in operating modes of the building and the HVAC system). This paper also addresses such issues as how model prediction is impacted by both improper model residuals and models identified from data periods which do not encompass the entire range of variation of both climatic conditions and the different building operating modes.

Impact of roof shading on building energy performance in warm & humid climatic places of India

2020 ◽  
pp. 50-64
Author(s):  
Kuladeep Kumar Sadevi ◽  
Avlokita Agrawal
Keyword(s):  
Energy Consumption ◽  
Energy Conservation ◽  
Energy Performance ◽  
General Assumption ◽  
Climatic Conditions ◽  
Building Envelope ◽  
Passive Cooling ◽  
Base Case ◽  
U Value ◽  
The Impact

With the rise in awareness of energy efficient buildings and adoption of mandatory energy conservation codes across the globe, significant change is being observed in the way the buildings are designed. With the launch of Energy Conservation Building Code (ECBC) in India, climate responsive designs and passive cooling techniques are being explored increasingly in building designs. Of all the building envelope components, roof surface has been identified as the most significant with respect to the heat gain due to the incident solar radiation on buildings, especially in tropical climatic conditions. Since ECBC specifies stringent U-Values for roof assembly, use of insulating materials is becoming popular. Along with insulation, the shading of the roof is also observed to be an important strategy for improving thermal performance of the building, especially in Warm and humid climatic conditions. This study intends to assess the impact of roof shading on building’s energy performance in comparison to that of exposed roof with insulation. A typical office building with specific geometry and schedules has been identified as base case model for this study. This building is simulated using energy modelling software ‘Design Builder’ with base case parameters as prescribed in ECBC. Further, the same building has been simulated parametrically adjusting the amount of roof insulation and roof shading simultaneously. The overall energy consumption and the envelope performance of the top floor are extracted for analysis. The results indicate that the roof shading is an effective passive cooling strategy for both naturally ventilated and air conditioned buildings in Warm and humid climates of India. It is also observed that a fully shaded roof outperforms the insulated roof as per ECBC prescription. Provision of shading over roof reduces the annual energy consumption of building in case of both insulated and uninsulated roofs. However, the impact is higher for uninsulated roofs (U-Value of 3.933 W/m2K), being 4.18% as compared to 0.59% for insulated roofs (U-Value of 0.33 W/m2K).While the general assumption is that roof insulation helps in reducing the energy consumption in tropical buildings, it is observed to be the other way when insulation is provided with roof shading. It is due to restricted heat loss during night.

Measurements of electric energy consumption in a low energy building in Slovak climatic conditions

2012 ◽  
Vol 7 (3) ◽  
pp. 23-32 ◽  
Author(s):  
Miloslav Bagoňa ◽  
Dušan Katunský ◽  
Martin Lopušniak ◽  
Marián Vertaľ
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Climatic Conditions ◽  
Low Energy ◽  
Electric Energy Consumption
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