scholarly journals Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

2019 ◽  
Author(s):  
Sina Faizollahzadeh ardabili ◽  
Amir Mosavi ◽  
Annamária R. Várkonyi-Kóczy
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Smart Cities ◽  
Building Energy ◽  
Learning Models ◽  
Information Demand ◽  
Building Energy Systems ◽  
Consumption Prediction ◽  
Energy Information ◽  
Machine Learning Models

Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.

scholarly journals Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6019
Author(s):  
José Manuel Lozano Domínguez ◽  
Faroq Al-Tam ◽  
Tomás de J. Mateo Sanguino ◽  
Noélia Correia
Keyword(s):  
Machine Learning ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Models ◽  
Fuzzy Classifier ◽  
Logistic Regression Models ◽  
The Road ◽  
Learning Agent ◽  
Machine Learning Models

Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier.

scholarly journals Energy demand prediction with machine learning supported by auto-tuning: a case study

2021 ◽  
Vol 2069 (1) ◽  
pp. 012143
Author(s):  
Sorana Ozaki ◽  
Ryozo Ooka ◽  
Shintaro Ikeda
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Random Search ◽  
Bayesian Optimization ◽  
Learning Models ◽  
Time Step ◽  
Efficient Operation ◽  
Tuning Methods ◽  
Machine Learning Models

Abstract The operational energy of buildings is making up one of the highest proportions of life-cycle carbon emissions. A more efficient operation of facilities would result in significant energy savings but necessitates computational models to predict a building’s future energy demands with high precision. To this end, various machine learning models have been proposed in recent years. These models’ prediction accuracies, however, strongly depend on their internal structure and hyperparameters. The time demand and expertise required for their finetuning call for a more efficient solution. In the context of a case study, this paper describes the relationship between a machine learning model’s prediction accuracy and its hyperparameters. Based on time-stamped recordings of outdoor temperatures and electricity demands of a hospital in Japan, recorded every 30 minutes for more than four years, using a deep neural network (DNN) ensemble model, electricity demands were predicted for sixty time steps to follow. Specifically, we used automatic hyperparameter tuning methods, such as grid search, random search, and Bayesian optimization. A single time step ahead, all tuning methods reduced the RSME to less than 50%, compared to non-optimized tuning. The results attest to machine learning models’ reliance on hyperparameters and the effectiveness of their automatic tuning.

scholarly journals Resource Usage and Performance Trade-offs for Machine Learning Models in Smart Environments

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1176 ◽  
Author(s):  
Davy Preuveneers ◽  
Ilias Tsingenopoulos ◽  
Wouter Joosen
Keyword(s):  
Machine Learning ◽  
Data Storage ◽  
Intelligent Systems ◽  
Smart Cities ◽  
Smart Environments ◽  
Learning Models ◽  
Resource Constrained ◽  
Network Bandwidth ◽  
Trade Offs ◽  
Machine Learning Models

The application of artificial intelligence enhances the ability of sensor and networking technologies to realize smart systems that sense, monitor and automatically control our everyday environments. Intelligent systems and applications often automate decisions based on the outcome of certain machine learning models. They collaborate at an ever increasing scale, ranging from smart homes and smart factories to smart cities. The best performing machine learning model, its architecture and parameters for a given task are ideally automatically determined through a hyperparameter tuning process. At the same time, edge computing is an emerging distributed computing paradigm that aims to bring computation and data storage closer to the location where they are needed to save network bandwidth or reduce the latency of requests. The challenge we address in this work is that hyperparameter tuning does not take into consideration resource trade-offs when selecting the best model for deployment in smart environments. The most accurate model might be prohibitively expensive to computationally evaluate on a resource constrained node at the edge of the network. We propose a multi-objective optimization solution to find acceptable trade-offs between model accuracy and resource consumption to enable the deployment of machine learning models in resource constrained smart environments. We demonstrate the feasibility of our approach by means of an anomaly detection use case. Additionally, we evaluate the extent that transfer learning techniques can be applied to reduce the amount of training required by reusing previous models, parameters and trade-off points from similar settings.

scholarly journals Tuning machine learning models for prediction of building energy loads

2019 ◽  
Vol 47 ◽  
pp. 101484 ◽  
Author(s):  
Saleh Seyedzadeh ◽  
Farzad Pour Rahimian ◽  
Parag Rastogi ◽  
Ivan Glesk
Keyword(s):  
Machine Learning ◽  
Building Energy ◽  
Learning Models ◽  
Machine Learning Models
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