scholarly journals Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6130
Author(s):  
Minseok Jang ◽  
Hyun-Cheol Jeong ◽  
Taegon Kim ◽  
Sung-Kwan Joo
Keyword(s):  
Dynamic Pricing ◽  
Mixed Logit ◽  
Electricity Consumption ◽  
Information Criterion ◽  
Gaussian Mixture ◽  
Optimal Number ◽  
Customer Segmentation ◽  
Smart Meters ◽  
Load Profile ◽  
Residential Electricity

Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.

scholarly journals How Does the Electricity Demand Profile Impact the Attractiveness of PV-Coupled Battery Systems Combining Applications?

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4038 ◽  
Author(s):  
Alejandro Pena-Bello ◽  
Edward Barbour ◽  
Marta C. Gonzalez ◽  
Selin Yilmaz ◽  
Martin K. Patel ◽  
...  
Keyword(s):  
Net Present Value ◽  
Electricity Consumption ◽  
Lithium Ion ◽  
Pv System ◽  
Peak Shaving ◽  
Load Profile ◽  
Battery Systems ◽  
Residential Electricity ◽  
Electricity Load ◽  
Demand Profile

Energy storage is a key solution to supply renewable electricity on demand and in particular batteries are becoming attractive for consumers who install PV panels. In order to minimize their electricity bill and keep the grid stable, batteries can combine applications. The daily match between PV supply and the electricity load profile is often considered as a determinant for the attractiveness of residential PV-coupled battery systems, however, the previous literature has so far mainly focused on the annual energy balance. In this paper, we analyze the techno-economic impact of adding a battery system to a new PV system that would otherwise be installed on its own, for different residential electricity load profiles in Geneva (Switzerland) and Austin (U.S.) using lithium-ion batteries performing various consumer applications, namely PV self-consumption, demand load-shifting, avoidance of PV curtailment, and demand peak shaving, individually and jointly. We employ clustering of the household’s load profile (with 15-minute resolution) for households with low, medium, and high annual electricity consumption in the two locations using a 1:1:1 sizing ratio. Our results show that with this simple sizing rule-of-thumb, the shape of the load profile has a small impact on the net present value of batteries. Overall, our analysis suggests that the effect of the load profile is small and differs across locations, whereas the combination of applications significantly increases profitability while marginally decreasing the share of self-consumption. Moreover, without the combination of applications, batteries are far from being economically viable.

scholarly journals Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Krzysztof Gajowniczek ◽  
Tomasz Ząbkowski
Keyword(s):  
Time Series ◽  
Similarity Measures ◽  
Optimal Number ◽  
Clustering Methods ◽  
Smart Meters ◽  
Practical Applications ◽  
Electricity Use ◽  
Residential Electricity ◽  
Using Data ◽  
Advanced Metering

Advanced metering infrastructures such as smart metering have begun to attract increasing attention; a considerable body of research is currently focusing on load profiling and forecasting at different scales on the grid. Electricity time series clustering is an effective tool for identifying useful information in various practical applications, including the forecasting of electricity usage, which is important for providing more data to smart meters. This paper presents a comprehensive study of clustering methods for residential electricity demand profiles and further applications focused on the creation of more accurate electricity forecasts for residential customers. The contributions of this paper are threefold: (1) using data from 46 homes in Austin, Texas, the similarity measures from different time series are analyzed; (2) the optimal number of clusters for representing residential electricity use profiles is determined; and (3) an extensive load forecasting study using different segmentation-enhanced forecasting algorithms is undertaken. Finally, from the operator’s perspective, the implications of the results are discussed in terms of the use of clustering methods for grouping electrical load patterns.

The diffusion of smart meters in France

2015 ◽  
Vol 8 (3) ◽  
pp. 231-244 ◽  
Author(s):  
Adnane Kendel ◽  
Nathalie Lazaric
Keyword(s):  
Energy Savings ◽  
Smart Cities ◽  
New Technology ◽  
Electricity Consumption ◽  
Smart Meters ◽  
Content Type ◽  
Residential Electricity ◽  
Low Efficiency ◽  
Residential Electricity Consumption ◽  
Quantitative Changes

Purpose – The purpose of this paper is to study business models (BMs) for smart meters (SMs) and discuss related issues in the French institutional context. Because SM introduce deregulation on both the demand and supply sides, the authors argue that they represent an opportunity to “unlock” the system by enabling feedback to consumers. The authors discuss the empirical findings from the TICELEC (Technologies de l’Information pour une Consommation Electrique – Information Technology for Sustainable Electricity Consumption Behaviors) project which is an experimental initiative to measure potential energy savings through the implementation of SM, and to test behavioral change. Design/methodology/approach – The empirical data are from the TICELEC project and refer to a municipality in southern France. The project was designed to show the qualitative changes deriving from a new technology, and the quantitative changes in the form of real reductions in residential electricity consumption in the short and medium terms. The authors discuss these changes and their potential replication, and examine the nature of the feedback provided to users and the implications for SM BMs for France and for smart cities more generally. Findings – The authors suggest that the opportunities provided by SM have to be compared with other kinds of intervention such as self-monitoring procedures. The results show that any intervention is important for moderating the sole impact of SM. The findings on the importance of changes to “energy habits” relate mainly to “curtailment” and “low efficiency” behaviors, which represent less costly changes. The lessons learned for BM developments linked to SM include incentive systems, smart tariffs, and technologies to increase potential behavior changes and energy savings in this field. Research limitations/implications – The authors’ analysis of the content of behavioral change shows that curtailment behavior and low-efficiency behavior remain dominant when SMs are implemented. Promoting high-efficiency behaviors is always difficult for reasons of cost. Thus, SM should be combined with other measures such as incentives systems, e.g. “smart tariffication,” and new services to increase their impact. Practical implications – A proper combination of smart tariffs and SMs to reduce peaks in demand would appear to be critical to boost SM development. It will also be important to integrate SMs with smart grids to improve energy efficiency and exploit renewables and energy storage in electricity networks. Social implications – SMs are important but any interventions that motivate households to change their energy habits also help in the French context. SMs enable households to try to reduce their energy consumption but they are not the solution. Originality/value – There are no detailed results published for France. Utilities such as Electricite Reseau Distribution France, have introduced R & D programs oriented to the deployment of SM which have been tested since 2009 (e.g. see the local LINKY meter projects in Lyon and Touraine). The empirical data are from the TICELEC project and refer to a municipality in southern France. The project was designed to show the qualitative changes deriving from a new technology, and the quantitative changes in the form of real reductions in residential electricity consumption in the short and medium terms. The authors discuss these changes and their potential replication, and examine the nature of the feedback provided to users and the implications for SM BMs for France and for smart cities more generally.

scholarly journals Regime changes in global sea surface salinity trend

2015 ◽  
Vol 12 (3) ◽  
pp. 983-1011 ◽  
Author(s):  
A. L. Aretxabaleta ◽  
K. W. Smith ◽  
J. Ballabrera-Poy
Keyword(s):  
Hydrological Cycle ◽  
Information Criterion ◽  
Gaussian Mixture ◽  
Optimal Number ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Regime Changes ◽  
Hadley Centre ◽  
The Uk

Abstract. Recent studies have shown significant sea surface salinity (SSS) changes at scales ranging from regional to global. In this study, we estimate global salinity means and trends using historical (1950–2014) SSS data from the UK Met. Office Hadley Centre objectively analyzed monthly fields and recent data from the SMOS satellite (2010–2014). We separate the different components (regimes) of the global surface salinity by fitting a Gaussian Mixture Model to the data and using Expectation–Maximization to distinguish the means and trends of the data. The procedure uses a non-subjective method (Bayesian Information Criterion) to extract the optimal number of means and trends. The results show the presence of three separate regimes: Regime A (1950–1990) is characterized by small trend magnitudes; Regime B (1990–2009) exhibited enhanced trends; and Regime C (2009–2014) with significantly larger trend magnitudes. The salinity differences between regime means were around 0.01. The trend acceleration could be related to an enhanced global hydrological cycle or to a change in the sampling methodology.

BAYESIAN SELF-ORGANIZING MAP FOR DATA CLASSIFICATION AND CLUSTERING

2013 ◽  
Vol 11 (05) ◽  
pp. 1350037 ◽  
Author(s):  
XIAOLIAN GUO ◽  
HAIYING WANG ◽  
DAVID H. GLASS
Keyword(s):  
Learning Process ◽  
Bayesian Learning ◽  
Nearest Neighbor ◽  
Information Criterion ◽  
Gaussian Mixture ◽  
Optimal Number ◽  
Support Vector ◽  
Self Organizing Map ◽  
K Nearest Neighbor ◽  
Self Organizing

The Bayesian self-organizing map (BSOM) has typically been used for density estimation. In this study, we implemented an adaptation of the model for performing unsupervized and supervised classification. In order to determine the optimal number of neurons to represent the given dataset during the learning process, an extended Bayesian learning process is proposed called the growing BSOM. It starts with two neurons and adds new neurons to the network via a process in which the neuron with the lowest individual log-likelihood is identified. The system has been tested using three synthetic datasets and one real dataset. The experimental results suggest that the BSOM-based approach can achieve better classification performance in comparisons to several widely-used models such as k-nearest neighbor (KNN), support vector machine (SVM) and Gaussian mixture model (GMM). By using the Bayesian information criterion (BIC) as a stopping criterion, the growing BSOM can model the data under study and estimate the number of clusters.

scholarly journals Sensor and Dynamic Pricing Aware Vertical Transportation in Smart Buildings

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Hyunkyoung Choi ◽  
Kyungwoon Cho ◽  
Hyokyung Bahn
Keyword(s):  
Dynamic Pricing ◽  
Electricity Consumption ◽  
Control Unit ◽  
Electricity Price ◽  
Human Beings ◽  
Smart Meters ◽  
Smart Buildings ◽  
Electricity Cost ◽  
Human Movements ◽  
Complex Optimization

In modern smart buildings, the electricity consumption of a building is monitored every time and costs differently at each time slot of a day. Smart buildings are also equipped with indoor sensors that can track the movement of human beings. In this paper, we propose a new elevator control system (ECS) that utilizes two kinds of context information in smart buildings: (1) human movements estimated by indoor sensors and (2) dynamic changes of electricity price. In particular, indoor sensors recognize elevator passengers before they press the elevator call buttons, and smart meters inform the dynamically changing price of the electricity to ECS. By using this information, our ECS aims at minimizing both the electricity cost and the waiting time of passengers. As this is a complex optimization problem, we use an evolutionary computation technique based on genetic algorithms (GA). We inject a learning module into the control unit of ECS, which monitors the change of the electricity price and the passengers’ traffic detected by sensors. Experimental results with the simulator we developed show that our ECS outperforms the scheduling configuration that does not consider sensor information or electricity price changes.

scholarly journals Bicycle Speed Modelling Considering Cyclist Characteristics, Vehicle Type and Track Attributes

2021 ◽  
Vol 12 (1) ◽  
pp. 43
Author(s):  
Xingchen Yan ◽  
Xiaofei Ye ◽  
Jun Chen ◽  
Tao Wang ◽  
Zhen Yang ◽  
...  
Keyword(s):  
Information Criterion ◽  
Lateral Position ◽  
Extreme Value ◽  
Optimal Number ◽  
Generalized Extreme Value ◽  
Vehicle Type ◽  
Lane Width ◽  
Bike Sharing ◽  
Mode Of Transport ◽  
Best Fit

Cycling is an increasingly popular mode of transport as part of the response to air pollution, urban congestion, and public health issues. The emergence of bike sharing programs and electric bicycles have also brought about notable changes in cycling characteristics, especially cycling speed. In order to provide a better basis for bicycle-related traffic simulations and theoretical derivations, the study aimed to seek the best distribution for bicycle riding speed considering cyclist characteristics, vehicle type, and track attributes. K-means clustering was performed on speed subcategories while selecting the optimal number of clustering using L method. Then, 15 common models were fitted to the grouped speed data and Kolmogorov–Smirnov test, Akaike information criterion, and Bayesian information criterion were applied to determine the best-fit distribution. The following results were acquired: (1) bicycle speed sub-clusters generated by the combinations of bicycle type, bicycle lateral position, gender, age, and lane width were grouped into three clusters; (2) Among the common distribution, generalized extreme value, gamma and lognormal were the top three models to fit the three clusters of speed dataset; and (3) integrating stability and overall performance, the generalized extreme value was the best-fit distribution of bicycle speed.

scholarly journals Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 740
Author(s):  
Hoshin V. Gupta ◽  
Mohammad Reza Ehsani ◽  
Tirthankar Roy ◽  
Maria A. Sans-Fuentes ◽  
Uwe Ehret ◽  
...  
Keyword(s):  
Sample Size ◽  
Parameter Tuning ◽  
Gaussian Mixture ◽  
Optimal Number ◽  
Small Sample ◽  
Random Samples ◽  
Data Points ◽  
Probability Mass ◽  
Entropy Estimate ◽  
Log Normal

We develop a simple Quantile Spacing (QS) method for accurate probabilistic estimation of one-dimensional entropy from equiprobable random samples, and compare it with the popular Bin-Counting (BC) and Kernel Density (KD) methods. In contrast to BC, which uses equal-width bins with varying probability mass, the QS method uses estimates of the quantiles that divide the support of the data generating probability density function (pdf) into equal-probability-mass intervals. And, whereas BC and KD each require optimal tuning of a hyper-parameter whose value varies with sample size and shape of the pdf, QS only requires specification of the number of quantiles to be used. Results indicate, for the class of distributions tested, that the optimal number of quantiles is a fixed fraction of the sample size (empirically determined to be ~0.25–0.35), and that this value is relatively insensitive to distributional form or sample size. This provides a clear advantage over BC and KD since hyper-parameter tuning is not required. Further, unlike KD, there is no need to select an appropriate kernel-type, and so QS is applicable to pdfs of arbitrary shape, including those with discontinuous slope and/or magnitude. Bootstrapping is used to approximate the sampling variability distribution of the resulting entropy estimate, and is shown to accurately reflect the true uncertainty. For the four distributional forms studied (Gaussian, Log-Normal, Exponential and Bimodal Gaussian Mixture), expected estimation bias is less than 1% and uncertainty is low even for samples of as few as 100 data points; in contrast, for KD the small sample bias can be as large as -10% and for BC as large as -50%. We speculate that estimating quantile locations, rather than bin-probabilities, results in more efficient use of the information in the data to approximate the underlying shape of an unknown data generating pdf.

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