Disaggregation Approach Research Articles

Gas disaggregation approach based on cluster analysis

Gas disaggregation approach based on cluster analysis

Additive biomass equations for slash pine trees: comparing three modeling approaches

Both aggregative and disaggregative strategies were used to develop additive nonlinear biomass equations for slash pine (Pinus elliottii Engelm. var. elliottii) trees in the southeastern United States. In the aggregative approach, the total tree biomass equation was specified by aggregating the expectations of component biomass models, and their parameters were estimated by jointly fitting all component and total biomass equations using weighted nonlinear seemingly unrelated regression (NSUR) (SUR1) or by jointly fitting component biomass equations using weighted NSUR (SUR2). In an alternative disaggregative approach (DRM), the biomass component proportions were modeled using Dirichlet regression, and the estimated total tree biomass was disaggregated into biomass components based on their estimated proportions. There was no single system to predict biomass that was best for all components and total tree biomass. The ranking of the three systems based on an array of fit statistics followed the order of SUR2 > SUR1 > DRM. All three systems provided more accurate biomass predictions than previously published equations.

Energy Disaggregation of Appliances Consumptions Using HAM Approach

Non-intrusive load monitoring (NILM) makes it possible for users to track the energy consumption of a household. In this paper, we present a new hybrid energy disaggregation approach named HAM. This event-based load disaggregation algorithm uses an improved multi-layer Hungarian algorithm to match appliances transient features and a supervised adaptive resonance theory mapping neural network (ARTMAP) to cluster steady features. This approach using a modified dual sliding window-based cumulative sum control chart algorithm (DSWC) to detect the transient event first, and we convert the multi-dimensional electrical features of appliances into a bipartite graph matching problem. This way, an improved Hungarian algorithm is proposed to find the perfect matching when appliances are in different states. For classification and identification, the ARTMAP network is used to learn and classify the steady features of appliances. Furthermore, we introduced a grey correlation evaluation and multiple matching strategies to increase the fitness and accuracy of the approach. Experimental results demonstrate that the proposed HAM outperforms the comparative algorithm in both our resident appliances dataset (RAD) and BLUED public dataset. The proposed approach could also facilitate NILM more applicable for common households.

Factors with Impact in the Assessment and Choice of Tourist Destinations

The preference disaggregation approach to the problem of multicriteria choice is to uncover the inherent model of choice that brings a decision unit to a conclusion in terms of an overall preference on the alternatives. In the context, the actual market shares of a class of competing products (alternatives) constitute a concise global variable originated in and reflecting the cumulative preferences of the consumer body, which affects the pattern of the market. This paper presents a systematic method to analyse the preference information cumulated in the actual market shares of competing products in terms of the criteria involved in the process of choice. The aim of this study is to present an entirely new approach to the problem of multicriteria choice and an application to analyse the market of tourist agency destination offer. In the paper outlines the market share analysis methodology for building an additive utility model in situations concerning attributes on which preferences behave monotonically. When contemporary information and communication technologies are included into the processes of choosing then electronic word-of-mouth (e-WOM) communication appears, while specific marketing messages are being created and transferred. The paper ends with some concluding remarks.

Semi-automatic disaggregation of a national resource inventory into a farm-scale soil depth class map

Knowledge of soil depth spatial variability is important for land use management especially in dryland agriculture regions, which rely on climate and soils to provide adequate water and nutrients during the growing season. Soil spatial variability can be predicted from legacy soil data through machine learning techniques producing quantitative soil maps requiring minimal resources. South Africa has a country wide 1:250,000 scale resource map known as the Land Type Survey (LTS) which includes soil properties such as soil depth, soil class, root limiting layer, clay content, and texture. Each LTS polygon (land type), is comprised of unique soil – terrain patterns and is therefore, not a true soil map. This study aims to disaggregate the LTS into a farm-scale soil depth class map through a two-step disaggregation approach. First, landform elements were predicted through a pattern recognition algorithm known as geomorphons. Geomorphons, together with the original LTS were overlaid to produce polygons with unique distributions of soil. The polygons were disaggregated further to produce a raster map of soil depth classes through a soil map disaggregation algorithm known as DSMART. The first most probable class raster achieved an accuracy of 68% and for the two most probable class rasters, an accuracy of 91% was achieved. The two-step approach proved necessary for producing a farm-scale soil map. The result of this study is significant as it produced a soil depth class map from a national resource map at a scale and resolution (10 m) suitable for farm management.

Development of soil moisture profiles through coupled microwave–thermal infrared observations in the southeastern United States

Abstract. The principle of maximum entropy (POME) can be used to develop vertical soil moisture (SM) profiles. The minimal inputs required by the POME model make it an excellent choice for remote sensing applications. Two of the major input requirements of the POME model are the surface boundary condition and profile-mean moisture content. Microwave-based SM estimates from the Advanced Microwave Scanning Radiometer (AMSR-E) can supply the surface boundary condition whereas thermal infrared-based moisture estimated from the Atmospheric Land EXchange Inverse (ALEXI) surface energy balance model can provide the mean moisture condition. A disaggregation approach was followed to downscale coarse-resolution (∼25 km) microwave SM estimates to match the finer resolution (∼5 km) thermal data. The study was conducted over multiple years (2006–2010) in the southeastern US. Disaggregated soil moisture estimates along with the developed profiles were compared with the Noah land surface model (LSM), as well as in situ measurements from 10 Natural Resource Conservation Services (NRCS) Soil Climate Analysis Network (SCAN) sites spatially distributed within the study region. The overall disaggregation results at the SCAN sites indicated that in most cases disaggregation improved the temporal correlations with unbiased root mean square differences (ubRMSD) in the range of 0.01–0.09 m3 m−3. The profile results at SCAN sites showed a mean bias of 0.03 and 0.05 (m3 m−3); ubRMSD of 0.05 and 0.06 (m3 m−3); and correlation coefficient of 0.44 and 0.48 against SCAN observations and Noah LSM, respectively. Correlations were generally highest in agricultural areas where values in the 0.6–0.7 range were achieved.

Stochastic Modelling of Parallel Process Flows in Intra-Logistics Systems: An Application in Automated Container Terminal

Many intra-logistics systems, such as automated container terminals, distribution warehouses, and crossdocks observe parallel process flows, which involve simultaneous (parallel) operations of multiple independent resources while processing a job. Due to the unknown service time of the job, stochastic modeling of the parallel process flows is quite complex. For modeling simplicity, researchers tend to assume sequential operations of the resources. This paper proposes a novel modeling approach using two-phase servers to model the simultaneous operations of resources. We develop a closed queuing network model to estimate the performance measures of a system which observes parallel process flows. To solve the resulting queuing network model, we construct two solution methods: an extended approximate mean value analysis and a network aggregation dis-aggregation approach. We derive insights on the accuracy of the solution methods using numerical experiments. Although both solution methods are quite accurate in estimating performance measures, the network aggregation dis-aggregation approach consistently performs best. We illustrate the proposed parallel modeling approach for two intra-logistic systems: an automated container terminal with automated guided vehicles and a robotic compact storage system. Results show that approximating the simultaneous operations as sequential operations underestimates the container terminal throughput on average by 28% and a maximum up to 47%. Similarly, considering sequential operations of the resources in the compact storage system results in underestimation of the throughput capacity up to 9%.

Monitoring time‐varying residential load operation modes: an efficient signal disaggregation approach

Monitoring time‐varying residential load operation modes: an efficient signal disaggregation approach

Solving the general map overlay problem using a fuzzy inference system designed for spatial disaggregation

Identification of mitigation and adaptation strategies in any situation has to be a well informed decision. This decision not only has to be based on quantifiable data (e.g. amounts or concentrations of emissions), but it also needs to consider spatial aspects such as the source of emissions, the impact of policies on the population or the identification of responsible parties. As such, it is important that the decisions are based on accurate reports made by experts in the field. Many data are available to the experts, and combining this data tends to increase the inherently contained uncertainty. Novel operations that exhibit a lower increase of uncertainty can yield outcomes that contain less uncertainty, which subsequently improves the accuracy of the resulting reports. At the same time, the decision-makers are confronted with different reports, and the comparison of the contained spatial aspects requires combining the data, which exhibits similar issues relating to uncertainty. With data usually represented in gridded structures, comparing them often requires a process called regridding: this is the process of mapping one grid onto a second grid, a process which increases spatial uncertainty. In this contribution, a novel regridding algorithm is presented. While mainly intended as a preprocessing tool for the experts, it is also applicable for supporting the comparison of gridded datasets as used by decision-makers. In the context of this article, a grid can be irregular, allowing the presented algorithm to also be used for remapping a grid onto, e.g. administrative borders or vice versa. The presented algorithm for regridding is a modification that is generally applicable on spatial disaggregation algorithms. It was developed in parallel with a novel method that uses artificial intelligence (in the form of fuzzy rule-based systems) to involve proxy data to obtain better results, and it will be demonstrated using this approach for spatial disaggregation. The methodology to perform regridding using an algorithm designed for spatial disaggregation is detailed in this article and the performance of the combination with the artificial intelligent system for disaggregation is illustrated by means of an example.

An Extensible Approach for Non-Intrusive Load Disaggregation With Smart Meter Data

Appliance-level load models are expected to be crucial to future smart grid applications. Unlike direct appliance monitoring approaches, it is more flexible and convenient to mine smart meter data to generate load models at device level nonintrusively and generalise to all households with smart meter ownership. This paper proposes a comprehensive and extensible framework to solve the load disaggregation problem for residential households. Our approach examines both the modelling of home appliances as hidden Markov models and the solving of non-intrusive load monitoring based on segmented integer quadratic constraint programming to disaggregate a household power profile into the appliance level. Structure of our approach to be implemented with current smart meter infrastructure is given and simulations are performed based on public datasets. All data are down-sampled to the rate that is consistent with the Australia smart meter infrastructure minimum functionality. The results demonstrate that our approach is able to work with existing smart meters to generate device level load model for other smart grid research and applications.

Religion and discrimination: extending the ‘disaggregative approach’

ABSTRACTCécile Laborde’s disaggregation strategy, which is convincingly applied to religion, liberal neutrality, and freedom of association, should be extended to discrimination, in order to more systematically determine whether, when, and why indirect religious discrimination is unfair. Moreover, while Laborde’s distinction between the ‘Disproportionate Burden scenario’ and the ‘Majority Bias scenario’ is a powerful alternative to the discrimination-focused account of the justifiability of religious exemptions, the epistemic status of that distinction is not immediately clear. A case can be made that Disproportionate Burden and Majority Bias do not map onto different types of minority exemption claims. They are perspectives or analytical frames that may jointly and usefully be applied to most instances of such claims.

An Iterative Load Disaggregation Approach Based on Appliance Consumption Pattern

Non-intrusive load monitoring (NILM), monitoring single-appliance consumption level by decomposing the aggregated energy consumption, is a novel and economic technology that is beneficial to energy utilities and energy demand management strategies development. Hardware costs of high-frequency sampling and algorithm’s computational complexity hampered NILM large-scale application. However, low sampling data shows poor performance in event detection when multiple appliances are simultaneously turned on. In this paper, we contribute an iterative disaggregation approach that is based on appliance consumption pattern (ILDACP). Our approach combined Fuzzy C-means clustering algorithm, which provide an initial appliance operating status, and sub-sequence searching Dynamic Time Warping, which retrieves single energy consumption based on the typical power consumption pattern. Results show that the proposed approach is effective to accurately disaggregate power consumption, and is suitable for the situation where different appliances are simultaneously operated. Also, the approach has lower computational complexity than Hidden Markov Model method and it is easy to implement in the household without installing special equipment.

Assessing cost‐effectiveness when environmental benefits are bundled: agricultural water management in Great Barrier Reef catchments

Using economic analysis to prioritise improvements in environmental conditions is particularly difficult when multiple benefits are involved. This includes ‘bundling’ issues in agricultural pollution management, where a change in management action or farming systems generates multiple improvements, such as reductions in more than one pollutant. In this study, we conceptualise and compare two different approaches to analysing cost‐effectiveness when varying bundles of benefits are generated for a single project investment. Each approach requires data to be transformed in some way to allow the analysis to proceed. The index approach requires the transformation on the benefits side so that the effects of multiple pollutant changes can be combined into a measure for each project which can then be compared to costs. By comparison, the disaggregation approach requires the transformation on the costs side where costs for each project have to be apportioned across the different pollutants involved. The paper provides novel insights with an application to agricultural water quality improvements into the Great Barrier Reef in Australia, demonstrating that while both approaches are effective in prioritising projects by cost‐effectiveness, the disaggregation approach provides more insightful results and values that may be relevant for use as upper value guidelines in future project selection.

Quantification of the changes in intensity and frequency of hourly extreme rainfall attributed climate change in Oman

Built environment, which includes some major investments in Oman, has been designed based on historical data and do not incorporate the climate change effects. This study estimates potential variations of the hourly annual maximum rainfall (AMR) in the future in Salalah, Oman. Of the five climate models, two were selected based on their ability to simulate local rainfall characteristics. A two-stage downscaling–disaggregation approach was applied. In the first stage, daily rainfall projections in 2040–2059 and 2080–2099 periods from MRI-CGCM3 and CNRM-CM5 models based on two Representative Concentration Pathways (RCP8.5 and RCP4.5) were downscaled to the local daily scale using a stochastic downscaling software (LARS-WG5.5). In the second stage, the stochastically downscaled daily rainfall time series were disaggregated using K-nearest neighbour technique into hourly series. The AMRs, extracted from 20 years of projections for four scenarios and two future periods were then fitted with the generalized extreme value distribution to obtain the rainfall intensity–frequency relationship. These results were compared with a similar relationship developed for the AMRs in baseline period. The results show that the reduction in number of wet days and increases in total rainfall will collectively intensify the future rainfall regime. A marked difference between future and historical intensity–frequency relationships was found with greater changes estimated for higher return periods. Furthermore, intensification of rainfall regime was projected to be stronger towards the end of the twenty-first century.

Nonlinear Multi attribute Satisfaction Analysis (N-MUSA): Preference disaggregation approach to satisfaction

Nonlinear MUSA is an extension of MUSA, which employs a derived approach to analyze customer satisfaction and its determinants. It is a preference disaggregation approach, widely welcomed by scholars since 2002, following the principles of ordinal regression analysis. N-MUSA as a goal programing model, evaluates the level of satisfaction among some groups including customers, employees, etcetera according to their values and expressed preferences. Using simple satisfaction survey data, N-MUSA aggregates the different preferences in a unique satisfaction function. The main advantage of this approach is to consider and convert the qualitative form of customer judgments and preferences in an ordinal scale based on a simple questionnaire to an interval scale, in the first place, and to develop various fruitful analytical indices in order to get more knowledge of customers in the second place. In spite of the abovementioned strengths, this paper tackles some computational shortcomings within MUSA and leads to the development of nonlinear form (N-MUSA), which is more effective and efficient in practice. This paper takes MUSA and its drawbacks into account, to introduce N-MUSA as a more efficient alternative, then, deploys it in numerical examples and a real case for more insights.

Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach

This paper investigates a non-intrusive approach of retrieving electric space heater (ESH) power profiles from a residential aggregated signal. In cold-climate regions with heating appliances controlled by electronic thermostats, an accurate non-intrusive recognition of power profiles is a challenging task. Accordingly, a robust disaggregation approach based on the difference factorial hidden Markov model (DFHMM) and the Kronecker operation is contributed. The proposed method aims to uncover the underlying stochastic tow-state models of ESHs using their common prior knowledge. The major advantage of the developed load-monitoring architecture consists of modeling simplicity and inference as well as load-detection efficacy in the presence of perturbations from other unknown loads. The experimental results prove the effectiveness of the method in manipulating the challenging case of multiple two-state loads with a high event overlapping probability.

A disaggregation approach for solving linear Diophantine equations

A disaggregation approach for solving linear Diophantine equations

Effective precipitation duration for runoff peaks based on catchment modelling

Despite precipitation intensities may greatly vary during one flood event, detailed information about these intensities may not be required to accurately simulate floods with a hydrological model which rather reacts to cumulative precipitation sums. This raises two questions: to which extent is it important to preserve sub-daily precipitation intensities and how long does it effectively rain from the hydrological point of view? Both questions might seem straightforward to answer with a direct analysis of past precipitation events but require some arbitrary choices regarding the length of a precipitation event. To avoid these arbitrary decisions, here we present an alternative approach to characterize the effective length of precipitation event which is based on runoff simulations with respect to large floods. More precisely, we quantify the fraction of a day over which the daily precipitation has to be distributed to faithfully reproduce the large annual and seasonal floods which were generated by the hourly precipitation rate time series. New precipitation time series were generated by first aggregating the hourly observed data into daily totals and then evenly distributing them over sub-daily periods (n hours). These simulated time series were used as input to a hydrological bucket-type model and the resulting runoff flood peaks were compared to those obtained when using the original precipitation time series. We define then the effective daily precipitation duration as the number of hours n, for which the largest peaks are simulated best. For nine mesoscale Swiss catchments this effective daily precipitation duration was about half a day, which indicates that detailed information on precipitation intensities is not necessarily required to accurately estimate peaks of the largest annual and seasonal floods. These findings support the use of simple disaggregation approaches to make usage of past daily precipitation observations or daily precipitation simulations (e.g. from climate models) for hydrological modeling at an hourly time step.

A hybrid approach for the spatial disaggregation of socio-economic indicators

While statistical information on socio-economic activities is widely available, the data are often collected or released only at a relatively aggregated level. In these aggregated forms, the data are useful for broad-scale assessments, although we often need to disaggregate the source data in order to provide more localized estimates, and in order to analyze correlations against geophysical variables. Spatial disaggregation techniques can be used in this context, to transform data from a set of source zones into a set of target zones, with different geometry and with a higher general level of spatial resolution. Still, few previous studies in the area have attempted to leverage state-of-the-art spatial disaggregation procedures in the context of socio-economic variables, instead focusing on applications related to population modeling. In this article, we report on experiments with a hybrid spatial disaggregation technique that combines state-of-the-art regression analysis procedures with the classic methods of dasymetric mapping and pycnophylactic interpolation. The hybrid procedure was used together with population density, land coverage, nighttime satellite imagery, and OpenStreetMap road density, as ancillary data to disaggregate different types of socio-economic indicators to a high-resolution grid. Our test specifically leveraged data relative to the Portuguese territory, resulting in the production of raster datasets with a resolution of 30 arc-seconds per cell. The article discusses the spatial disaggregation methodology and the quality of the obtained results under different experimental conditions.

An iterative aggregation and disaggregation approach to the calculation of steady state distributions of continuous processes

A mapping of the process on a continuous configuration space to the symbolic representation of the motion on a discrete state space will be combined with an iterative aggregation and disaggregation (IAD) procedure to obtain steady state distributions of the process. The IAD speeds up the convergence to the unit eigenvector, which is the steady state distribution, by forming smaller aggregated matrices whose unit eigenvector solutions are used to refine approximations of the steady state vector until convergence is reached. This method works very efficiently and can be used together with distributed or parallel computing methods to obtain high resolution images of the steady state distribution of complex atomistic or energy landscape type problems. The method is illustrated in two numerical examples. In the first example the transition matrix is assumed to be known. The second example represents an overdamped Brownian motion process subject to a dichotomously changing external potential.