Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering

Li, Kehua; Ma, Zhenjun; Robinson, Duane; Ma, Jun

doi:10.1016/j.apenergy.2018.09.050

Cited by 116 publications

(43 citation statements)

References 41 publications

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“…In the second step, the data obtained from the landscape analysis of noise maps were analyzed by means of multivariate analysis; the hierarchical cluster analysis (HCA), using the Ward’s linkage method, was employed to categorize stands in accordance with their noise propagation characteristics. Cluster analysis is a powerful tool which can effectively group similar objects while ensuring their distinction from other grouped objects (Li et al, 2018). In this study, the Euclidean distance was used as the dissimilarity measurement because it is the best known and most often used way of calculating the distance between samples (da Silva Torres, Garbelotti & Neto, 2006).…”

Section: Methodsmentioning

confidence: 99%

Incorporating acoustic objectives into Forest Management Planning when sensitive bird species are relevant

Iglesias-Merchán

Ortiz-Urbina

Ezquerro

et al. 2019

PeerJ

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Background The potentially negative effects of timber harvesting on biodiversity and habitat conservation leads to the consideration of a wide range of restrictions to forest logging in natural areas. In particular, high noise levels produced by forest machinery present a challenge to developing sustainable forest management plans. The Cinereous vulture (Aegypius monachus), the largest bird of prey whose nests are located in mature trees, is considered to be appropriate as an indicator species for environment-friendly forest planning. In this work, we evaluated spatially differences in sound propagation between stands. We hypothesized that differences due to the influence of orography in mountainous forests would allow the relaxation of spatial and temporary restrictions to timber logging, without causing any great disturbance at nesting sites of sensitive species. Methods Our study was conducted in a Scots pine (Pinus sylvestris) forest of Spain, where an important colony of the Western European population of Cinereous vulture is located. We built 62 noise maps to characterize noise pollution due to tree logging at planning level. We modeled two different scenarios, in order to characterize; (i) the effect of a chainsaw operator during a complete cycle for felling a tree (Scenario 1), and (ii) the effect of the peak level produced by the breaking noise emitted by the trunk of the tree and its impact on the ground (Scenario 2). A strategy of three logical steps was designed; (i) landscape-scale analysis of noise propagation in stands, (ii) hierarchical cluster analysis of stands, (iii) assessment of the potentially significant influence of noise management in timber harvesting. Results The minimum distance (DIST) from chainsaw operator sites to the 40 dB(A) contour lines was the only variable that had a significant influence on the clustering results. On the other hand, mean values of a newly proposed metric called average radius or radial distance (ARD) oscillated between 174 m in cluster #1 (Scenario 1) and 407 m in cluster #2 (Scenario 2). Discussion Our results highlight the convenience of considering noise modeling tools at a forest planning level in order to address the compatibility of forest logging and the necessity of protecting nature. We found that spatial propagation of noise made by chainsaws at felling sites does not differ between stands even in a mountainous terrain, contrary to what we initially hypothesized. However, temporary logging restrictions could be excluded in about 36% of the current conditioned management areas according to ARD results in Scenario 2 (400 m). This proposal could be based on a sound pressure level (in decibels) criteria instead of conventional buffer protection distance criteria. In addition, it is suggested that the current size of restricted management areas could be generally extended from a 100 m radius to a 200 m one around the Cinereous vulture nest sites.

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Section: Methodsmentioning

confidence: 99%

Incorporating acoustic objectives into Forest Management Planning when sensitive bird species are relevant

Iglesias-Merchán

Ortiz-Urbina

Ezquerro

et al. 2019

PeerJ

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“…Then, a neural network is utilized as a load forecasting model [16]. Recently, a categorical load decomposition approach was proposed, where quadratic programming (QP) was employed to separate categorical profiles from various mixtures of customer load profiles [10], and Gaussian mixture model and hierarchical clustering were applied to identify the categorical building load with two-step clustering [17]. Alternatively, a Physarum-based hybrid optimization algorithm was suggested, which provides adaptable solutions for the loadshedding problem in a microgrid system [18].…”

Section: A Short-term Load Forecasting (Stlf)mentioning

confidence: 99%

Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals

2019

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In this paper, a characteristic load decomposition (CLD)-based day-ahead load forecasting scheme is proposed for a mixed-use complex. The aggregated load of the complex is composed of the mixtures of different electricity usage patterns, and short-term load forecasting can be implemented by summing disaggregated sub-load predictions. However, tracing all usage patterns of sub-loads for prediction may be infeasible because of limited resources for measurement and analysis. To prevent this infeasibility, the proposed scheme focuses on effective decomposition using the sub-loads of typical characteristic load profiles and their representative pilot signals. Separate forecasts are obtained for the decomposed characteristic sub-loads using a hybrid scheme, which combines day-type conditioned linear prediction with long short-term memory regressions. Complex campus load data are considered to evaluate the proposed CLD-based hybrid forecasting. The evaluation results show that the proposed scheme outperforms conventional hybrid or similar-day-based forecasting approaches. Even when sub-load measurements are available only for a limited period, the CLD scheme can be applied for the extended training data through virtual disaggregations. INDEX TERMS Day-ahead load forecasting, time series analysis, long short-term memory, hybrid forecasting model, characteristic load decomposition, hierarchical load forecasting.

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“…As a novel approach in this work, obtained reference buildings will be defined not only by the characteristic value, but also by a definition of the energy consumption throughout a natural year, resulting as truly useful in order to increase the accuracy of the consumption estimation over the short-term [34,35]. Seasonal variations may also then be observed and, in some cases, this may help to find abnormal energetic behaviors from the dynamics of the consumption point of view [36].…”

Section: Building Sustainability Energy Indexes and Annual Electricmentioning

confidence: 99%

Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study

et al. 2018

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Health Services building stock is usually the top energy consumer in the Administrative sector, by a considerable margin. Therefore, energy consumption supervision, prediction, and improvement should be carried out for this group in a preferential manner. Most prior studies in this field have characterized the energy consumption of buildings based on complex simulations, which tend to be limited by modelisation restrictions and assumptions. In this paper, an improved method for the clusterization of buildings based on their electrical energy consumption is proposed and, then, reference profiles are determined by examining the variation of energy consumption over the typical yearly consumption period. The temporary variation has been analyzed by evaluating the temporary evolution of the area consumption index through data mining and statistical clusterization techniques. The proposed methodology has been applied to building stock of the Health Services in the Castilla y León region in Spain, based on three years of historical monthly electrical energy consumption data for over 250 buildings. This building stock consists of hospitals, health centers (with and without emergency services) and a miscellaneous set of administrative and residential buildings. Results reveal five distinct electrical consumption profiles that have been associated with five reference buildings, permitting significant improvement in the demand estimation as compared to merely using the classical energy consumption indicators.

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Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering

Cited by 116 publications

References 41 publications

Incorporating acoustic objectives into Forest Management Planning when sensitive bird species are relevant

Incorporating acoustic objectives into Forest Management Planning when sensitive bird species are relevant

Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals

Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study

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