393 research outputs found
Novel analysis–forecast system based on multi-objective optimization for air quality index
© 2018 Elsevier Ltd The air quality index (AQI) is an important indicator of air quality. Owing to the randomness and non-stationarity inherent in AQI, it is still a challenging task to establish a reasonable analysis–forecast system for AQI. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to improve both aspects simultaneously, leading to unsatisfactory results. In this study, a novel analysis–forecast system is proposed that consists of complexity analysis, data preprocessing, and optimize–forecast modules and addresses the problems of air quality monitoring. The proposed system performs a complexity analysis of the original series based on sample entropy and data preprocessing using a novel feature selection model that integrates a decomposition technique and an optimization algorithm for removing noise and selecting the optimal input structure, and then forecasts hourly AQI series by utilizing a modified least squares support vector machine optimized by a multi-objective multi-verse optimization algorithm. Experiments based on datasets from eight major cities in China demonstrated that the proposed system can simultaneously obtain high accuracy and strong stability and is thus efficient and reliable for air quality monitoring
Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine
It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the highfrequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction
Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine
It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction
Optimal planning and sizing of an autonomous hybrid energy system using multi stage grey wolf optimization
The continuous increase in energy demand and the perpetual dwindling of fossil fuel coupled with its environmental impact have recently attracted research focus in harnessing renewable energy sources (RES) across the globe. Representing the largest RES, solar and wind energy systems are expanding due to the growing evidence of global warming phenomena. However, variability and intermittency are some of the main features that characterize these RES as a result of fluctuation in weather conditions. Hybridization of multiple sources improves the system’s efficiency and reliability of supply due to the varying nature of the RES. Also, the unavailability of solar radiation (SR) and wind speed (WS) measuring equipment in the meteorological stations necessitates the development of prediction algorithms based on Artificial Intelligent (AI) techniques. This thesis presents an autonomous hybrid renewable energy system for a remote community. The hybrid energy system comprises of a photovoltaic module and wind turbine as the main source of energy. Batteries are used as the energy storage devices and diesel generator as a backup energy supply. A new hybrid Wavelet Transform and Adaptive Neuro-Fuzzy Inference System (WT-ANFIS) is developed for the SR prediction, while a hybrid Particle Swarm Optimization (PSO) and ANFIS (PSO-ANFIS) algorithm is developed for the WS prediction. The prediction accuracy of the proposed WT-ANFIS model was validated by comparison with the conventional ANFIS model, Genetic Algorithm (GA) and ANFIS (GA-ANFIS), and PSO-ANFIS models. The proposed PSO-ANFIS for the WS prediction is also compared with ANFIS and GA-ANFIS models. Also, Root Mean Square Error (RMSE), Correlation Coefficient (r) and Coefficient of Determination (R²) are used as statistical indicators to evaluate the performance of the developed prediction models. Additionally, a techno-economic feasibility analysis is carried out using the SR and WS data predicted to assess the viability of the hybrid solar-wind-battery-diesel system for electricity generation in the selected study area. Finally, a new cost-effective Multi Stage – Grey Wolf Optimization (MS-GWO) algorithm is applied to optimally size the different system components. This is aimed at minimizing the net present cost (NPC) while considering reliability and satisfying the load demand. MS-GWO is evaluated by comparison with PSO, GWO and PSO-GWO algorithms. From the results obtained, the statistical evaluators used for model performance assessment of the SR prediction shows that the hybrid WT-ANFIS model’s accuracy outperforms the PSO-ANFIS model by 65% RMSE and 9% R². Also, from the simulation results, the optimal configuration has an NPC of 0.110/kWh, with an operating cost of $4,723. The system is environmentally friendly with a renewable fraction of 98.3% and greenhouse gas emission reduction of 65%. Finally, a comparison is done between the proposed MS-GWO algorithm with the PSO, GWO and PSO-GWO algorithms. Based on this comparison, the proposed hybrid MS-GWO algorithm outperforms the individual PSO, GWO and PSO-GWO by 3.17%, 2.53% and 2.11% in terms of NPC and reduces the computational time by 53%, 46% and 36% respectively. Therefore, it can be concluded that the proposed MS-GWO technique can be applied for optimal sizing application globally
Applied Metaheuristic Computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Decarbonization cost of Bangladesh's energy sector: Influence of corruption
As a rapidly developing lower-middle income country, Bangladesh has been maintaining
a steady growth of +5% in the gross domestic product (GDP) annually since
2004, eventually reaching 7.1% in 2016. The country is targeting to become uppermiddle-
income and developed by 2021 and 2041 respectively, which translates to an
annual GDP growth rate of 7.58% during this period. The bulk of this growth
is expected to come from the manufacturing sector, the significant shift towards
which started at the turn of this century. Energy intensity of manufacturing-based
growth is higher, the evidence of which can be seen in the 3.17 times increase in
national energy consumption between 2001 and 2014. Also, Bangladesh aims to
achieve 100% electrification rate by 2021 against an annual population growth rate
of 1.08%. With the increasing per capita income, there is now a growing middle
class fuelling the growth in demand for convenient forms of energy. Considering
the above drivers, the Bangladesh 2050 Pathways Model suggested 35 times higher
energy demand than that of 2010 by 2050. The government and private sector have
started a substantial amount of investments in the energy sector to meet the signi
ficant future demand. Approximately US250 billion in 2050 under HCS, which can be reduced 23% under
ZCS. The cost of decarbonization would be 3.6, 3.4 and 3.2 times under average
cost of MCS, LCS, and ZCS, than that of HCS. As the energy sector of Bangladesh
is under rapid development, the accumulated capital would be comparatively high
by 2050. However, fuel cost can be significantly reduced under LCS and ZCS which
would also ensure lower emissions. The study suggested that energy mix change,
technological maturity, corruption and demand reduction can influence the cost
of decarbonization. However, the most significant influencer for the decarbonization
of Bangladeshi energy sector would be the corruption. Results showed that if
Bangladesh can minimize the effect of corruption on the energy sector, it can reduce
the cost of decarbonization 45-77% by 2050 under MCS, LCS, and ZCS
Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement
Volume measurement plays an important role in the production and processing of food products. Various methods have been
proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction
comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction
have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs
volume measurements using random points. Monte Carlo method only requires information regarding whether random points
fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a
computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with
heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images.
Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from
binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the
water displacement method. In addition, the proposed method is more accurate and faster than the space carving method
Applied Methuerstic computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
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