scholarly journals More Buildings Make More Generalizable Models—Benchmarking Prediction Methods on Open Electrical Meter Data

2019 ◽  
Vol 1 (3) ◽  
pp. 974-993 ◽  
Author(s):  
Clayton Miller
Keyword(s):  
Machine Learning ◽  
Primary Schools ◽  
Residential Buildings ◽  
Building Energy ◽  
Genome Project ◽  
Prediction Methods ◽  
Data Sets ◽  
Energy Prediction ◽  
Data Prediction ◽  
Prediction Techniques

Prediction is a common machine learning (ML) technique used on building energy consumption data. This process is valuable for anomaly detection, load profile-based building control and measurement and verification procedures. Hundreds of building energy prediction techniques have been developed over the last three decades, yet there is still no consensus on which techniques are the most effective for various building types. In addition, many of the techniques developed are not publicly available to the general research community. This paper outlines a library of open-source regression techniques from the Scikit-Learn Python library and describes the process of applying them to open hourly electrical meter data from 482 non-residential buildings from the Building Data Genome Project. The results illustrate that there are several techniques, notably decision tree-based models, that perform well on two-thirds of the total cohort of buildings. However, over one-third of the buildings, specifically primary schools, performed poorly. This example implementation shows that there is no one size-fits-all modeling solution and that various types of temporal behavior are difficult to capture using machine learning. An analysis of the generalizability of the models tested motivates the need for the application of future techniques to a board range of building types and behaviors. The importance of this type of scalability analysis is discussed in the context of the growth of energy meter and other Internet-of-Things (IoT) data streams in the built environment. This framework is designed to be an example baseline implementation for other building energy data prediction methods as applied to a larger population of buildings. For reproducibility, the entire code base and data sets are found on Github.

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 Applications of Machine Learning in Building Energy Prediction and Savings

2019 ◽  
Vol 10 (1) ◽  
pp. 1-10
Author(s):  
Priyan Rai ◽  
Dr. Nabil Nassif ◽  
Kevin Eaton ◽  
Alexander Rodrigues
Keyword(s):  
Machine Learning ◽  
Building Energy ◽  
Energy Prediction ◽  
Applications Of Machine Learning

Machine learning for early stage building energy prediction: Increment and enrichment

2021 ◽  
Vol 304 ◽  
pp. 117787
Author(s):  
Manav Mahan Singh ◽  
Sundaravelpandian Singaravel ◽  
Philipp Geyer
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Building Energy ◽  
Energy Prediction

EMPIRICAL ASSESSMENT OF MACHINE LEARNING BASED SOFTWARE DEFECT PREDICTION TECHNIQUES

2008 ◽  
Vol 17 (02) ◽  
pp. 389-400 ◽  
Author(s):  
VENKATA UDAYA B. CHALLAGULLA ◽  
FAROKH B. BASTANI ◽  
I-LING YEN ◽  
RAYMOND A. PAUL
Keyword(s):  
Machine Learning ◽  
Feature Subset Selection ◽  
Defect Prediction ◽  
Data Sets ◽  
Feature Subset ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Intelligent Software ◽  
Instance Based Learning ◽  
Prediction Techniques

Automated reliability assessment is essential for systems that entail dynamic adaptation based on runtime mission-specific requirements. One approach along this direction is to monitor and assess the system using machine learning-based software defect prediction techniques. Due to the dynamic nature of software data collected, Instance-based learning algorithms are proposed for the above purposes. To evaluate the accuracy of these methods, the paper presents an empirical analysis of four different real-time software defect data sets using different predictor models. The results show that a combination of 1R and Instance-based learning along with Consistency-based subset evaluation technique provides a relatively better consistency in achieving accurate predictions as compared with other models. No direct relationship is observed between the skewness present in the data sets and the prediction accuracy of these models. Principal Component Analysis (PCA) does not show a consistent advantage in improving the accuracy of the predictions. While random reduction of attributes gave poor accuracy results, simple Feature Subset Selection methods performed better than PCA for most prediction models. Based on these results, the paper presents a high-level design of an Intelligent Software Defect Analysis tool (ISDAT) for dynamic monitoring and defect assessment of software modules.

scholarly journals Coupling simulation with artificial neural networks for the optimisation of HVAC controls in manufacturing environments

2020 ◽  
Author(s):  
Victoria Jayne Mawson ◽  
Ben Richard Hughes
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Energy Demand ◽  
Large Data ◽  
Electrical Energy ◽  
Building Energy ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Unseen Data ◽  
Manufacturing Environments

Abstract Manufacturing remains one of the most energy intensive sectors, additionally, the energy used within buildings for heating, ventilation and air conditioning (HVAC) is responsible for almost half of the UK’s energy demand. Commonly, these are analysed in isolation from one another. Use of machine learning is gaining popularity due to its ability to solve non-linear problems with large data sets and little knowledge about relationships between parameters. Such models use relationships between inputs and outputs to make further predictions on unseen data, without requiring any understanding regarding the system, making them highly suited to dealing with the stochastic data sets found in a manufacturing environment. This has been seen in literature for determining electrical energy demand for residential or commercial buildings, rather than manufacturing environments. This study proposes a novel method of coupling simulation with machine learning to predict indoor workshop conditions and building energy demand, in response to production schedules, outdoor conditions, building behaviour and use. Such predictions can subsequently allow for more efficient management of HVAC systems. Based upon predicted energy consumption, potential spikes were identified and manufacturing schedules subsequently optimised to reduce peak energy demand. Coupling simulation techniques with machine learning algorithms eliminates the requirement for costly and intrusive methods of data collection, providing a method of predicting and optimising building energy consumption in the manufacturing sector.

scholarly journals The Impact of Data Segmentation in Predicting Monthly Building Energy Use with Support Vector Regression

2021 ◽  
pp. 69-76
Author(s):  
William Mounter ◽  
Huda Dawood ◽  
Nashwan Dawood
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Energy Use ◽  
Building Energy ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Energy Usage ◽  
Energy Prediction ◽  
Learning Techniques

AbstractAdvances in metering technologies and machine learning methods provide both opportunities and challenges for predicting building energy usage in the both the short and long term. However, there are minimal studies on comparing machine learning techniques in predicting building energy usage on their rolling horizon, compared with comparisons based upon a singular forecast range. With the majority of forecasts ranges being within the range of one week, due to the significant increases in error beyond short term building energy prediction. The aim of this paper is to investigate how the accuracy of building energy predictions can be improved for long term predictions, in part of a larger study into which machine learning techniques predict more accuracy within different forecast ranges. In this case study the ‘Clarendon building’ of Teesside University was selected for use in using it’s BMS data (Building Management System) to predict the building’s overall energy usage with Support Vector Regression. Examining how altering what data is used to train the models, impacts their overall accuracy. Such as by segmenting the model by building modes (Active and dormant), or by days of the week (Weekdays and weekends). Of which it was observed that modelling building weekday and weekend energy usage, lead to a reduction of 11% MAPE on average compared with unsegmented predictions.

scholarly journals Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 433 ◽  
Author(s):  
Seokho Kim ◽  
Yujin Sung ◽  
Yoondong Sung ◽  
Donghyun Seo
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Building Energy ◽  
Energy Model ◽  
Prediction Performance ◽  
Energy Modeling ◽  
Modeling Tools ◽  
Energy Prediction ◽  
Occupancy Estimation ◽  
Input Variables

To improve the energy prediction performance of a building energy model, the occupancy status information is very important. This is more important in real buildings, rather than under construction buildings, because actual building occupancy can significantly influence its energy consumption. In this study, a machine learning based framework for a consecutive occupancy estimation is proposed by utilizing internet of things data, such as indoor temperature and luminance, CO2 density, electricity consumption of lighting, HVAC (heating, ventilation, and air conditioning), electric appliances, etc. Three machine learning based occupancy estimation algorithms (decision tree, support vector machine, artificial neural networks) are selected and evaluated in terms of the performance of estimating the occupancy status for each season. The selection process of the input variables that have crucial impact on the algorithms’ performance are described in detail. Finally, an occupancy estimation framework that can repeat model training and estimation consecutively in a situation when time-series data are continuously provided over the entire measurement period is suggested. In addition, the performance of the framework is evaluated to identify how it improves the energy prediction performance of the building energy model compared to conventional energy modeling practices. The suggested framework is distinguished from similar previous studies in two ways: 1) The proposed framework reveals that input variables for the occupancy estimation model can be occasionally changed by an occupant response to certain times and seasons, and 2) the framework incorporates time-series indirect occupancy sensing data and classification algorithms to consecutively provide occupancy information for the energy modeling effort.

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