Modeling of highways energy consumption with artificial intelligence and regression methods

Author(s):  
Ö. F. Cansiz ◽  
F. Üneş ◽  
İ. Erginer ◽  
B. Taşar
2021 ◽  
Vol 13 (23) ◽  
pp. 13016
Author(s):  
Rami Naimi ◽  
Maroua Nouiri ◽  
Olivier Cardin

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.


Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3440 ◽  
Author(s):  
Chin-Chi Cheng ◽  
Dasheng Lee

The study continues the theoretical derivation from Part 1, and the experiment is carried out at a bus station equipped with six water-cooled chillers. Between 2012 and 2017, historical data collected from temperature and humidity sensors, as well as the energy consumption data, were used to build artificial intelligence (AI) assisted heating ventilation and air conditioning (HVAC) control models. The AI control system, in conjunction with a specifically designed prior information notice (PIN) sensor, was used to improve the prediction accuracy. This data collected between 2012 and 2016 was used for AI training and PIN sensor testing. During the hottest week of 2017 in Taiwan, the PIN sensor was used to conduct temperature and humidity data predictions. A model-based predictive control was developed to obtain air conditioning energy consumption data. The comparative results between the predictive and actual data showed that the temperature and humidity prediction accuracies were between 95.5 and 96.6%, respectively. Additionally, energy savings amounting to 39.8% were achieved compared to the theoretical estimates of 44.6%, a difference of less than 5%. These results show that the experimental model supports the theoretical estimations. In the future, a PIN sensor will be installed in a chiller to further verify the energy savings of the AI assisted HVAC control.


2021 ◽  
Vol 288 ◽  
pp. 01067
Author(s):  
Elena Troianova ◽  
Evgenia Lerman ◽  
Elena Baliasnikova ◽  
Ina Fiutik ◽  
Ekaterina Savelieva

The article deals with the application of modern artificial intelligence technologies that affect the economic efficiency of generating companies. Scientific novelty lies in the approach to the consideration of artificial intelligence as both external and internal factors of influence on the dynamics of production and consumption of electricity. As a result of the study, the key aspects of the growth of the economic efficiency of the activities of energy generating companies are highlighted and characterized. The forecast of the prospective sustainable development of certain areas of the energy sector and the increase in energy consumption is presented.


2021 ◽  
pp. 1-19
Author(s):  
Cristóvão Sousa ◽  
Daniel Teixeira ◽  
Davide Carneiro ◽  
Diogo Nunes ◽  
Paulo Novais

As the availability of computational power and communication technologies increases, Humans and systems are able to tackle increasingly challenging decision problems. Taking decisions over incomplete visions of a situation is particularly challenging and calls for a set of intertwined skills that must be put into place under a clear rationale. This work addresses how to deliver autonomous decisions for the management of a public street lighting network, to optimize energy consumption without compromising light quality patterns. Our approach is grounded in an holistic methodology, combining semantic and Artificial Intelligence principles to define methods and artefacts for supporting decisions to be taken in the context of an incomplete domain. That is, a domain with absence of data and of explicit domain assertions.


2020 ◽  
Vol 10 (19) ◽  
pp. 6903
Author(s):  
Meysam Nouri ◽  
Parveen Sihag ◽  
Farzin Salmasi ◽  
Ozgur Kisi

In this study, the energy dissipation of cascade spillways was studied by conducting a series of laboratory experiments. Five spillways slope angles (α) (10°, 20°, 30°, 40°, and 50°), various step numbers (N) ranging from 4 to 75, and a wide range of discharges (Q), were considered. Some data-based models were developed to explain the relationships between hydraulic parameters. Multiple linear and nonlinear regression-based equations were developed based on dimensional analysis theory to compute energy dissipation over cascade spillways. For testing the robustness of developed data-based models, M5P, stochastic M5P, and random forest (RF) were used as new artificial intelligence (AI)-based techniques. To relate the input and output variables of energy dissipation, AI-based and regression approaches were developed. It was found that the formulation based on the stochastic M5P approach in solving energy dissipation problems over cascade spillways is more successful than the other regression and AI-based methods. Sensitivity analysis suggests that spillway slope in degrees (α) is the most influential input variable in predicting the relative energy dissipation (%) of the spillway in comparison to other input variables.


Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4044
Author(s):  
Inyeop Choi ◽  
Hyogon Kim

The mobile terminals used in the logistics industry can be exposed to wildly varying environments, which may hinder effective operation. In particular, those used in cold storages can be subject to frosting in the scanner window when they are carried out of the warehouses to a room-temperature space outside. To prevent this, they usually employ a film heater on the scanner window. However, the temperature and humidity conditions of the surrounding environment and the temperature of the terminal itself that cause frosting vary widely. Due to the complicated frost-forming conditions, existing industrial mobile terminals choose to implement rather simple rules that operate the film heater well above the freezing point, which inevitably leads to inefficient energy use. This paper demonstrates that to avoid such waste, on-device artificial intelligence (AI) a.k.a. edge AI can be readily employed to industrial mobile terminals and can improve their energy efficiency. We propose an artificial-intelligence-based approach that utilizes deep learning technology to avoid the energy-wasting defrosting operations. By combining the traditional temperature-sensing logic with a convolutional neural network (CNN) classifier that visually checks for frost, we can more precisely control the defrosting operation. We embed the CNN classifier in the device and demonstrate that the approach significantly reduces the energy consumption. On our test terminal, the net ratio of the energy consumption by the existing system to that of the edge AI for the heating film is almost 14:1. Even with the common current-dissipation accounted for, our edge AI system would increase the operating hours by 86%, or by more than 6 h compared with the system without the edge AI.


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