Bauhaus-Institut für zukunftsweisende Infrastruktursysteme (b.is)
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A Hybrid Clustering and Classification Technique for Forecasting Short-Term Energy Consumption
(2018)
Electrical energy distributor companies in Iran have to announce their energy demand at least three 3-day ahead of the market opening. Therefore, an accurate load estimation is highly crucial. This research invoked methodology based on CRISP data mining and used SVM, ANN, and CBA-ANN-SVM (a novel hybrid model of clustering with both widely used ANN and SVM) to predict short-term electrical energy demand of Bandarabbas. In previous studies, researchers introduced few effective parameters with no reasonable error about Bandarabbas power consumption. In this research we tried to recognize all efficient parameters and with the use of CBA-ANN-SVM model, the rate of error has been minimized. After consulting with experts in the field of power consumption and plotting daily power consumption for each week, this research showed that official holidays and weekends have impact on the power consumption. When the weather gets warmer, the consumption of electrical energy increases due to turning on electrical air conditioner. Also, con-sumption patterns in warm and cold months are different. Analyzing power consumption of the same month for different years had shown high similarity in power consumption patterns. Factors with high impact on power consumption were identified and statistical methods were utilized to prove their impacts. Using SVM, ANN and CBA-ANN-SVM, the model was built. Sine the proposed method (CBA-ANN-SVM) has low MAPE 5 1.474 (4 clusters) and MAPE 5 1.297 (3 clusters) in comparison with SVM (MAPE 5 2.015) and ANN (MAPE 5 1.790), this model was selected as the final model. The final model has the benefits from both models and the benefits of clustering. Clustering algorithm with discovering data structure, divides data into several clusters based on similarities and differences between them. Because data inside each cluster are more similar than entire data, modeling in each cluster will present better results. For future research, we suggest using fuzzy methods and genetic algorithm or a hybrid of both to forecast each cluster. It is also possible to use fuzzy methods or genetic algorithms or a hybrid of both without using clustering. It is issued that such models will produce better and more accurate results.
This paper presents a hybrid approach to predict the electric energy usage of weather-sensitive loads. The presented methodutilizes the clustering paradigm along with ANN and SVMapproaches for accurate short-term prediction of electric energyusage, using weather data. Since the methodology beinginvoked in this research is based on CRISP data mining, datapreparation has received a gr eat deal of attention in thisresear ch. Once data pre-processing was done, the underlyingpattern of electric energy consumption was extracted by themeans of machine learning methods to precisely forecast short-term energy consumption. The proposed approach (CBA-ANN-SVM) was applied to real load data and resulting higher accu-racy comparing to the existing models.
2018 American Institute of Chemical Engineers Environ Prog, 2018
https://doi.org/10.1002/ep.12934
In the early 2000s the pre-Columbian, anthropologically produced black soil in the Amazon basin, „Terra Preta de Índio“, received greater scientific attention. Compared to the surrounding poor soils, this very fertile anthrosol contains significantly higher levels of microorganisms and nutrients. The reason for this was determined to be the likewise high levels of charred biomass. This stable carbon, now called biochar, has since been intensively examined as an option to improve soil and to store carbon.
Although the creation of Terra Preta was most likely based on a purposeful utilization of organic residues from households and gardens, biochar plays no role in the current recycling of bio-waste. However, the implementation of biochar could lead to many improvements. Results from agricultural research suggest that not only the yield capacity of soils can be increased but also the process performance of composting and biogas plants.
The latter is especially relevant since currently about 40% of all collected bio-waste in Germany is recycled in an energy-material cascade consisting of anaerobic digestion and composting. The use of biochar in this cascade could then sequentially increase biogas yields, reduce greenhouse gas emissions, and improve compost quality.
To realize the aforementioned advantages, the concept of biochar has to be integrated into the existing bio-waste cascade as practically as possible. This was done by the development of a theoretical scenario that allowed the analysis of energy and material flows to evaluate biochar’s recycling performance. Furthermore, the legal and economic framework were examined to assess the feasibility of the extended cascade and to suggest possible adjustments to the frameworks.
We propose an enhanced iterative scheme for the precise reconstruction of piezoelectric material parameters from electric impedance and mechanical displacement measurements. It is based on finite-element simulations of the full three-dimensional piezoelectric equations, combined with an inexact Newton or nonlinear Landweber iterative inversion scheme. We apply our method to two piezoelectric materials and test its performance. For the first material, the manufacturer provides a full data set; for the second one, no material data set is available. For both cases, our inverse scheme, using electric impedance measurements as input data, performs well.
The world society faces a huge challenge to implement the human right of “access to sanitation”. More and more it is accepted that the conventional approach towards providing sanitation services is not suitable to solve this problem. This dissertation examines the possibility to enhance “access to sanitation” for people who are living in areas with underdeveloped water and wastewater infrastructure systems. The idea hereby is to follow an integrated approach for sanitation, which allows for a mutual completion of existing infrastructure with resource-based sanitation systems.
The notion “integrated sanitation system (iSaS)” is defined in this work and guiding principles for iSaS are formulated. Further on the implementation of iSaS is assessed at the example of a case study in the city of Darkhan in Mongolia. More than half of Mongolia’s population live in settlements where yurts (tents of Nomadic people) are predominant. In these settlements (or “ger areas”) sanitation systems are not existent and the hygienic situation is precarious.
An iSaS has been developed for the ger areas in Darkhan and tested over more than two years. Further on a software-based model has been developed with the goal to describe and assess different variations of the iSaS. The results of the assessment of material-flows, monetary-flows and communication-flows within the iSaS are presented in this dissertation. The iSaS model is adaptable and transferable to the socio-economic conditions in other regions and climate zones.