Optimization of waste management regions using recursive Thiessen polygons

Richter, Amy; Ng, Kelvin Tsun Wai; Karimi, Nima; Peng, Wu; Kashani, Armin Hajighasem

doi:10.1016/j.jclepro.2019.06.178

Cited by 31 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of MLP-ANN time series-based have been evaluated by statical criteria such as mean squared error (MSE), root mean squared error (RMSE), and standard deviation (STD) as described in (9)(10)(11). Moreover, the regression results of forecasting results have been depicted to have a better understanding of forecasting accuracies [43]- [45].…”

Section: ) Level-2: the Proposed Corrective Very Short-term Lfmentioning

confidence: 99%

Optimal Day-Ahead Scheduling and Operation of the Prosumer by Considering Corrective Actions Based on Very Short-Term Load Forecasting

et al. 2020

View full text Add to dashboard Cite

Energy management systems (EMSs) play an important role in the optimal operation of prosumers. As an essential segment of each EMS, the load forecasting (LF) block enhances the optimal utilization of renewable energy sources (RESs) and battery energy storage systems (BESSs). In this paper, a new optimal day-ahead scheduling and operation of the prosumer is proposed based on the two-level corrective LF. The proposed two-level corrective LF actions are developed through a very precise shortterm LF. In the first level, a time-series LF is applied using multi-layer perceptron artificial neural networks (MLP-ANNs). In order to improve the accuracy of the forecasted load data at the first level, the second level corrective LF is applied using feed-forward (FF) ANNs. The second stage prediction is initiated when the LF results violate the pre-defined criteria. The proposed method is applied to a prosumer under different cases (based on the consideration of BESS operation behaviors and cost) and various scenarios (based on the accuracy of the load data). The obtained optimal day-ahead operation results illustrate the advantages of the proposed method and its corrective forecasting process. The comparison of the obtained results and those of other available ones show the effectiveness of the proposed optimal operation of the prosumers. The advantages of the proposed method are highlighted while the BESS costs are considered.

show abstract

Section: ) Level-2: the Proposed Corrective Very Short-term Lfmentioning

confidence: 99%

Optimal Day-Ahead Scheduling and Operation of the Prosumer by Considering Corrective Actions Based on Very Short-Term Load Forecasting

et al. 2020

View full text Add to dashboard Cite

show abstract

“…However, alternative approaches that break down such administrative constructs can also be achieved by using methods, such as Thiessen polygons ( Karimi et al, 2020 , Richter et al, 2019 ; Richter, Ng, Karimi, & Li, 2021; Zhang et al, 2015 ) or Standard Deviational Ellipses (Richter, Ng, Karimi, & Chang, 2021).…”

Section: Introductionmentioning

confidence: 99%

Impact of COVID-19 lockdown measures on waste production behavior in Lisbon

et al. 2022

View full text Add to dashboard Cite

The recent restrictions on mobility and economic activities imposed by governments due to the COVID-19 pandemic have significantly affected waste production and recycling patterns in cities worldwide. This effect differed both between cities and within cities as the measures of confinement adopted by governments had diverse impacts in different areas of cities, depending on their characteristics (e.g., touristic, or residential). In the present work, mixed waste collection areas were created, based on waste collection points, that define spatial units in which contextual data such as tourism and residential characteristics were aggregated. The difference in mixed waste collected compared with previous years was analyzed along with the impacts on recycling due to the modification in operations regarding waste collection during the lockdown. The results showed that despite the suspension of the door-to-door recycling system during the lockdown, this did not translate into an increase in the production of mixed waste, and the recycling levels of previous years have not been reached after the lockdown, indicating a possible change in recycling habits in Lisbon. The touristic and non-residential mixed waste circuits presented significantly reduced mixed waste production compared to the non-pandemic context. Also, tourist, mobility, and economic activity were measured to understand which factors contributed to waste production changes during the COVID-19 pandemic. While little evidence of a relationship with these exogenous variables was found at the citywide level, evidence was found at the waste collection circuit level.

show abstract

“…The methods of lumping transformation include the arithmetic average method [ 13 ], Thiessen polygon method, and isoline method [ 14 , 15 ], whose process and principle are relatively simple, so we will not repeat them here. The following studies are carried out on feature extraction and the preprocessing of missing values, complex nonlinear feature factors, and linear correlation feature factors in the obtained feature sequence.…”

Section: Research On Runoff Driving Factor Mining Based On Big Data Analysismentioning

confidence: 99%

Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data

Ting

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Starting from the main eco-environmental problems faced by water environment, taking Yanhe River Basin as an example, this paper discusses the theoretical connotation and evaluation calculation method of eco-environmental water consumption. In order to study the eco-environmental water consumption of Yanhe River Basin, a runoff driving factor mining method based on big data analysis is established in this paper. Aiming at the problem that the statistical law and genetic law of runoff change frequently in changing environment, the mining technology method of runoff key driving factors is proposed by combining traditional methods with big data analysis. The characteristic factors that have no significant impact on runoff change are removed, the implicit characteristic factors affecting runoff change are extracted, the driving relationship of hydrological, meteorological, and vegetation characteristic factors on ecological water consumption change is identified, and the key driving factors of ecological water consumption change are extracted, which lays a data foundation for ecological water consumption prediction based on machine learning. The economic water consumption based on eco-environmental water consumption in Yanhe River Basin in the future is predicted (including water demand in three aspects of industry, agriculture, and life); that is, the prediction is to meet the economic water demand on the basis of ensuring that the water consumption of ecological environment will not be occupied, which can effectively ensure the improvement of ecological environment function in Yanhe River Basin and is conducive to the sustainable utilization of water resources in Yanhe River Basin. The research is only based on a small watershed such as Yanhe River Basin, and the purpose of the research is to provide a reference for ecological environment protection and sustainable utilization of water resources in the Loess Plateau, even in the arid, semiarid, and semihumid areas of North China.

show abstract

Optimization of waste management regions using recursive Thiessen polygons

Cited by 31 publications

References 27 publications

Optimal Day-Ahead Scheduling and Operation of the Prosumer by Considering Corrective Actions Based on Very Short-Term Load Forecasting

Optimal Day-Ahead Scheduling and Operation of the Prosumer by Considering Corrective Actions Based on Very Short-Term Load Forecasting

Impact of COVID-19 lockdown measures on waste production behavior in Lisbon

Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data

Contact Info

Product

Resources

About