Using Supervised Machine Learning to Explore Energy Consumption Data in Private Sector Housing

2015 ◽  
pp. 320-333 ◽  
Author(s):  
Mariya Sodenkamp ◽  
Konstantin Hopf ◽  
Thorsten Staake
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Classification Accuracy ◽  
Residential Buildings ◽  
Supervised Machine Learning ◽  
Household Characteristics ◽  
Consumption Data ◽  
Feature Filtering ◽  
Electricity Load ◽  
Class Labels

Smart electricity meters allow capturing consumption load profiles of residential buildings. Besides several other applications, the retrieved data renders it possible to reveal household characteristics including the number of persons per apartment, age of the dwelling, etc., which helps to develop targeted energy conservation services. The goal of this chapter is to develop further related methods of smart meter data analytics that infer such household characteristics using weekly load curves. The contribution of this chapter to the state of the art is threefold. The authors first quadruplicate the number of defined features that describe electricity load curves to preserve relevant structures for classification. Then, they suggest feature filtering techniques to reduce the dimension of the input to a set of a few significant ones. Finally, the authors redefine class labels for some properties. As a result, the classification accuracy is elevated up to 82%, while the runtime complexity is significantly reduced.

scholarly journals Analyzing Load Profiles of Energy Consumption to Infer Household Characteristics Using Smart Meters

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 773 ◽  
Author(s):  
Muhammad Fahim ◽  
Alberto Sillitti
Keyword(s):  
Energy Consumption ◽  
Time Series Data ◽  
Residential Buildings ◽  
Series Data ◽  
Smart Meters ◽  
Learning Mechanism ◽  
Efficient Energy ◽  
Household Characteristics ◽  
Univariate Time Series ◽  
Consumption Data

The increasing penetration of smart meters provides an excellent opportunity to monitor and analyze energy consumption in residential buildings. In this paper, we propose a framework to process the observed profiles of energy consumption to infer the household characteristics in residential buildings. Such characteristics can be used for improving resource allocation and for an efficient energy management that will ultimately contribute to reducing carbon dioxide (CO 2 ) emission. Our approach is based on automated extraction of features from univariate time-series data and development of a model through a variant of the decision trees technique (i.e., ensemble learning mechanism) random forest. We process and analyzed energy consumption data to answer four primitive questions. To evaluate the approach, we performed experiments on publicly available datasets. Our experiments show a precision of 82% and a recall of 81% in inferring household characteristics.

scholarly journals Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM

2021 ◽  
Vol 10 (7) ◽  
pp. 436
Author(s):  
Amerah Alghanim ◽  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Classification Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Semantic Inference ◽  
Road Type ◽  
The Impact

Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set.

Machine learning-based analysis of occupant-centric aspects: Critical elements in the energy consumption of residential buildings

2021 ◽  
pp. 103846
Author(s):  
Rashed Alsharif ◽  
Mehrdad Arashpour ◽  
Emadaldin Mohammadi Golafshani ◽  
M. Reza Hosseini ◽  
Victor Chang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Residential Buildings ◽  
Critical Elements

scholarly journals Analysis of Office Rooms Energy Consumption Data in Respect to Meteorological and Direct Sun Exposure Conditions

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7590
Author(s):  
Adam Kula ◽  
Albert Smalcerz ◽  
Maciej Sajkowski ◽  
Zygmunt Kamiński
Keyword(s):  
Energy Consumption ◽  
Public Service ◽  
Residential Buildings ◽  
Sun Exposure ◽  
Building Energy ◽  
The West ◽  
Building Energy Management ◽  
Consumption Data ◽  
Office Rooms ◽  
The Impact

There are many papers concerning the consumption of energy in different buildings. Most describe residential buildings, with only a few about office- or public service buildings. Few articles showcase the use of energy consumption in specific rooms of a building, directed in different geographical directions. On the other hand, many publications present methods, such as machine learning or AI, for building energy management and prediction of its consumption. These methods have limitations and represent a certain level of uncertainty. In order to compare energy consumption of different rooms, the measurements of particular building-room parameters were collected and analyzed. The obtained results showcase the effect of room location, regarding geographical directions, for the consumption of energy for heating. For south-exposed rooms, due to sun radiation, it is possible to switch heating off completely, and even overheating of 3 °C above the 22 °C temperature set point occurs. The impact of the sun radiation for rooms with a window directed east or west reached about 1 °C and lasts for a few hours before noon for the east, and until late afternoon for the west.

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

2021 ◽  
pp. 036119812110391
Author(s):  
Joseph Severino ◽  
Yi Hou ◽  
Ambarish Nag ◽  
Jacob Holden ◽  
Lei Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Real Time ◽  
Volume Estimation ◽  
Energy Estimates ◽  
Volume Data ◽  
Road Transportation ◽  
Energy Efficient Routing ◽  
Real World Data ◽  
Consumption Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

scholarly journals Building energy modeling at neighborhood scale

2020 ◽  
Vol 13 (7) ◽  
pp. 1353-1386 ◽  
Author(s):  
Guglielmina Mutani ◽  
Valeria Todeschi
Keyword(s):  
Energy Consumption ◽  
Energy Balance ◽  
Residential Buildings ◽  
Energy Performance ◽  
Average Error ◽  
Monthly Basis ◽  
Support Tool ◽  
Steady State Method ◽  
Consumption Data ◽  
Neighborhood Scale

Abstract The urban climate and outdoor air quality of cities that have a positive thermal balance depending on the thermal consumptions of buildings cause an increase of the urban heat island and global warming effects. The aim of this work has been to develop an energy balance using the energy consumption data of the district heating network. The here presented engineering energy model is at a neighborhood scale, and the energy-use results have been obtained from a heat balance of residential buildings, by means of a quasi-steady state method, on a monthly basis. The modeling approach also considers the characteristics of the urban context that may have a significant effect on its energy performance. The model includes a number of urban variables, such as solar exposition and thermal radiation lost to the sky of the built environment. This methodology was applied to thirty-three 1 km × 1 km meshes in the city of Turin, using the monthly energy consumption data of three consecutive heating seasons. The results showed that the model is accurate for old built areas; the average error is 10% for buildings constructed before 1970, while the error reaches 20% for newer buildings. The importance and originality of this study are related to the fact that the energy balance is applied at neighborhood scale and urban parameters are introduced with the support of a GIS tool. The resulting engineering models can be applied as a decision support tool for citizens, public administrations, and policy makers to evaluate the distribution of energy consumptions and the relative GHG emissions to promote a more sustainable urban environment. Future researches will be carried out with the aim of introducing other urban variables into the model, such as the canyon effect and the presence of vegetation.

scholarly journals Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

2020 ◽  
Vol 10 (11) ◽  
pp. 3829 ◽  
Author(s):  
Arash Moradzadeh ◽  
Amin Mansour-Saatloo ◽  
Behnam Mohammadi-Ivatloo ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Residential Buildings ◽  
Load Forecasting ◽  
Training Data ◽  
Support Vector ◽  
Cooling Load ◽  
Heating And Cooling ◽  
Cooling Loads ◽  
Heating And Cooling Load

Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

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