Smart city platform development for an automated waste collection system
CL Popa, G Carutasu, CE Cotet, NL Carutasu… - Sustainability, 2017 - mdpi.com
Nowadays, governments and companies are looking for solutions to increase the collection
level of various waste types by using new technologies and devices such as smart sensors …
level of various waste types by using new technologies and devices such as smart sensors …
Influencing factors evaluation of machine learning-based energy consumption prediction
Modern computing resources, including machine learning-based techniques, are used to
maintain stability between the demand and supply of electricity. Machine learning is widely …
maintain stability between the demand and supply of electricity. Machine learning is widely …
Short-term passenger flow prediction based on wavelet transform and kernel extreme learning machine
R Liu, Y Wang, H Zhou, Z Qian - Ieee Access, 2019 - ieeexplore.ieee.org
In view of the instability and complexity of passenger flow change in urban rail transit, it is
the key and the difficult point to use the prediction model to get more accurate number of …
the key and the difficult point to use the prediction model to get more accurate number of …
Developing a mixed neural network approach to forecast the residential electricity consumption based on sensor recorded data
In this paper, we report a study having as a main goal the obtaining of a method that can
provide an accurate forecast of the residential electricity consumption, refining it up to the …
provide an accurate forecast of the residential electricity consumption, refining it up to the …
Blockchain-based power energy trading management
Distributed peer-to-peer power energy markets are emerging quickly. Due to central
governance and lack of effective information aggregation mechanisms, energy trading …
governance and lack of effective information aggregation mechanisms, energy trading …
Analyses of distributed generation and storage effect on the electricity consumption curve in the smart grid context
The householders' electricity consumption is about 20–30% of the total consumption that is a
significant space for demand response. Mainly, the householders are becoming more and …
significant space for demand response. Mainly, the householders are becoming more and …
Use of neural network based prediction algorithms for powering up smart portable accessories
Emerging Trends in the use of smart portable accessories, particularly within the context of
the Internet of Things (IoT), where smart sensor devices are employed for data gathering …
the Internet of Things (IoT), where smart sensor devices are employed for data gathering …
Prediction of Turkish mutual funds' net asset value using the fund portfolio distribution
Accurate prediction of mutual funds' net asset value (NAV) has become increasingly
important for investors. Mutual fund investors will be significantly supported by the …
important for investors. Mutual fund investors will be significantly supported by the …
Smart cities and awareness of sustainable communities related to demand response programs: data processing with first-order and hierarchical confirmatory factor …
The mentality of electricity consumers is one of the most important entities that must be
addressed when dealing with issues in the operation of power systems. Consumers are …
addressed when dealing with issues in the operation of power systems. Consumers are …
Designing, developing and validating a forecasting method for the month ahead hourly electricity consumption in the case of medium industrial consumers
DM Petroșanu - Processes, 2019 - mdpi.com
An accurate forecast of the electricity consumption is particularly important to both
consumers and system operators. The purpose of this study is to develop a forecasting …
consumers and system operators. The purpose of this study is to develop a forecasting …