Conventional models and artificial intelligence-based models for energy consumption forecasting: A review

2019 ◽  
Vol 181 ◽  
pp. 106187 ◽  
Author(s):  
Nan Wei ◽  
Changjun Li ◽  
Xiaolong Peng ◽  
Fanhua Zeng ◽  
Xinqian Lu
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Conventional Models

scholarly journals A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021 ◽  
Vol 13 (23) ◽  
pp. 13016
Author(s):  
Rami Naimi ◽  
Maroua Nouiri ◽  
Olivier Cardin
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Job Shop ◽  
Learning Algorithm ◽  
Minimum Energy ◽  
Flexible Job Shop ◽  
Q Learning ◽  
Minimum Energy Consumption ◽  
Machine Failure ◽  
Flexible Job Shop Problem

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.

scholarly journals Artificial Intelligence Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 2. Prior Information Notice (PIN) Sensor Design and Simulation Results

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3440 ◽  
Author(s):  
Chin-Chi Cheng ◽  
Dasheng Lee
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Air Conditioning ◽  
Prior Information ◽  
Energy Savings ◽  
Theoretical Derivation ◽  
Consumption Data ◽  
Temperature And Humidity ◽  
Hvac Control ◽  
Comparative Results

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.

scholarly journals Artificial intelligence technologies as a factor in increasing the economic efficiency of generating companies

2021 ◽  
Vol 288 ◽  
pp. 01067
Author(s):  
Elena Troianova ◽  
Evgenia Lerman ◽  
Elena Baliasnikova ◽  
Ina Fiutik ◽  
Ekaterina Savelieva
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Energy Consumption ◽  
Economic Efficiency ◽  
Energy Sector ◽  
Internal Factors ◽  
Production And Consumption ◽  
Factors Of Influence ◽  
Key Aspects

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.

scholarly journals Prediction Model of Compressive Strength Development in Concrete Containing Four Kinds of Gelled Materials with the Artificial Intelligence Method

2019 ◽  
Vol 9 (6) ◽  
pp. 1039 ◽  
Author(s):  
Guohua Liu ◽  
Jian Zheng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Compressive Strength ◽  
Bp Neural Network ◽  
Strength Development ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Compressive Strength Development ◽  
Compressive Strength Of Concrete ◽  
Conventional Models

Green concrete has been widely used in recent years because its production compliments environmental conservation. The prediction of the compressive strength of concrete using non-destructive techniques is of interest to engineers worldwide. Such methods are easy to carry out because they require little or no sample preparation. Conventional models and artificial intelligence models are two main types of models to predict the compressive strength of concrete. Artificial intelligence models main include the artificial neural network (ANN) model, back propagation (BP) neural network model, fuzzy model etc. Since both conventional models and artificial intelligence models are flawed. This study proposes to build a concrete compressive strength development over time (CCSDOT) model by using conventional method combined with the artificial intelligence method. The CCSDOT model performed well in predicting and fitting the compressive strength development in green concrete containing cement, slag, fly ash, and limestone flour. It is concluded that the CCSDOT model is stable through the use of sensitivity analysis. To evaluate the precision of this model, the prediction results of the proposed model were compared to that of the model based on the BP neural network. The results verify that the recommended model enjoys better flexibility, capability, and accuracy in predicting the compressive strength development in concrete than the other models.

scholarly journals Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)

2020 ◽  
Vol 12 (2) ◽  
pp. 698 ◽  
Author(s):  
Maolin Cheng ◽  
Jiano Li ◽  
Yun Liu ◽  
Bin Liu
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Clean Energy ◽  
Equation Model ◽  
Grey Model ◽  
Population Increase ◽  
Factors Affecting ◽  
Comparison Results ◽  
Conventional Models ◽  
Improved Model

Forecasting China’s clean energy consumption has great significance for China in making sustainably economic development strategies. Because the main factors affecting China’s clean energy consumption are economic scale and population size, and there are three variables in total, this paper tries to simulate and forecast China’s clean energy consumption using the grey model GM (1, 3). However, the conventional grey GM (1, N) model has great simulation and forecasting errors, the main reason for which is the structural inconsistency between the grey differential equation for parameter estimation and the whitening equation for forecasting. In this case, this paper improves the conventional model and provides an improved model GM (1, N). The modeling results show that the improved grey model GM (1, N) built with the method proposed improves simulation and forecasting precision greatly compared with conventional models. To compare the model with other forecasting models, this paper builds a grey GM (1, 1) model, a regression model and a difference equation model. The comparison results show that the improved grey model GM (1, N) built with the method proposed shows simulation and forecasting precision superior to that of other models as a whole. In the final section, the paper forecasts China’s clean energy consumption from 2019 to 2025 using the improved grey model GM (1, N). The forecasting results show that, by 2025, China’s clean energy consumption shall reach the equivalent of 1.504976082 billion tons of standard coal. From 2019 to 2025, clean energy consumption shall increase by 11.32% annually on average, far above the economic growth rate, indicating China’s economic growth shall have a great demand for clean energy in the future. Studies have shown that China’s clean energy consumption shall increase rapidly with economic growth and population increase in the next few years.

Knowledge-based decision intelligence in street lighting management

2021 ◽  
pp. 1-19
Author(s):  
Cristóvão Sousa ◽  
Daniel Teixeira ◽  
Davide Carneiro ◽  
Diogo Nunes ◽  
Paulo Novais
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Light Quality ◽  
Communication Technologies ◽  
Decision Problems ◽  
Computational Power ◽  
Street Lighting ◽  
Knowledge Based

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.

scholarly journals An On-Device Deep Learning Approach to Battery Saving on Industrial Mobile Terminals

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4044
Author(s):  
Inyeop Choi ◽  
Hyogon Kim
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Energy Consumption ◽  
Energy Use ◽  
Freezing Point ◽  
Learning Technology ◽  
Mobile Terminals ◽  
Logistics Industry ◽  
Varying Environments ◽  
Current Dissipation

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.

scholarly journals Artificial intelligence methods to control the energy efficiency of electric rolling stock online

2020 ◽  
Vol 175 ◽  
pp. 05046
Author(s):  
Vasily Cheremisin ◽  
Stanislav Istomin ◽  
Artem Perestenko
Keyword(s):  
Artificial Intelligence ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Simulation Models ◽  
Automated System ◽  
Rolling Stock ◽  
Artificial Intelligence Methods ◽  
Electric Rolling Stock ◽  
Motion Parameters ◽  
Consumption Control

The international practices in organizing the energy consumption control of electric rolling stock are analyzed. As a result, it was concluded that currently the issue of organizing the energy consumption control of electric rolling stock is mainly solved by using analytical methods. These methods are based on designing the simulation models, which are usually based on the Pontryagin maximum principle. However, considering the development of recording systems for motion parameters of electric rolling stock, as well as other automated systems of Russian Railways, it seems promising to develop and study artificial intelligence methods and algorithms for solving real-time monitoring issues of electric rolling stock energy consumption. It was also determined that the most modern motion parameter recorders have a number of significant drawbacks from the data analysis point of view. Such drawbacks include insufficient data and their low reliability, lack of linking the recorded data to trips and locomotive teams, the impossibility of choosing a constant interval for recording measurement results. Moreover, there is also high probability of errors when recording data on the cartridge, lack of GPS/GLONASS satellite navigation system, lack of wireless data transmission, imperfection of software and inconvenience of exporting data from a cartridge file and its incompleteness. In order to test the energy efficiency assessment of electric rolling stock within the limits of arbitrary energy tracking areas, the Corresponding software was developed on the basis of data from the motion parameters recorders. However, developing the new complex automated system is required for the full implementation of the proposed consumption tracking method. Such system should combine the entire set of measured parameters, both for electric rolling stock and for the traction power supply system.

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