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1 |
Exploring the viability of a new ‘pay-as-you-use’ energy management model in budget hotels
کاوش در بقای یک مدل مدیریت انرژی جدید "در عوض استفاده می کنید" در هتل های ارزان قیمت-2020 Hotels consume significant amounts of energy, especially in guest rooms. Financial incentives can be given to hotel guests for conserving energy during their stay while financial penalties can be applied for excessive energy use. This can be achieved by deploying the smart energy meters (SEMs) in guest rooms that enable accurate energy monitoring and billing. This study explored the viability of a new business model for energy management in hotels underpinned by SEMs. Semi-structured interviews with managers of UK budget hotels revealed the determinants of industrial adoption of this new model. Despite positive appeal, the chances for the model’s immediate commercialisation were found slim due to its novelty and the market disruption potential held. To enhance the business viability of the proposed model, close integration of energy conservation targets into the corporate agenda of budget hotels is necessary coupled with dedicated policy support. Keywords: Budget hotel | Energy efficiency | Smart metering | technology | Financial (dis)incentive | Behavioral change |
مقاله انگلیسی |
2 |
A pattern recognition methodology for analyzing residential customers load data and targeting demand response applications
یک روش تشخیص الگو برای تجزیه و تحلیل داده های باربری مشتریان مسکونی و هدف قرار دادن برنامه های پاسخ تقاضا-2019 The availability of smart meter data allows defining innovative applications such as demand response (DR) programs for households. However, the dimensionality of data imposes challenges for the data min- ing of load patterns. In addition, the inherent variability of residential consumption patterns is a major problem for deciding on the characteristic consumption patterns and implementing proper DR settle- ments. In this regard, this paper utilizes a data size reduction and clustering methodology to analyze residential consumption behavior. Firstly, the distinctive time periods of household activity during the day are identified. Then, using these time periods, a modified symbolic aggregate approximation (SAX) technique is utilized to transform the load patterns into symbolic representations. In the next step, by applying a clustering method, the major consumption patterns are extracted and analyzed. Finally, the customers are ranked based on their stability over time. The proposed approach is applied on a large dataset of residential customers’ smart meter data and can achieve three main goals: 1) it reduces the dimensionality of data by utilizing the data size reduction, 2) it alleviates the problems associated with the clustering of residential customers, 3) its results are in accordance with the needs of systems oper- ators or demand response aggregators and can be used for demand response targeting. The paper also provides a thorough analysis of different aspects of residential electricity consumption and various ap- proaches to the clustering of households which can inform industry and research activity to optimize smart meter operational use Keywords: Clustering algorithms | Demand response | Load patterns | Smart meters | Symbolic aggregate approximation (SAX) |
مقاله انگلیسی |
3 |
A shape-based clustering method for pattern recognition of residential electricity consumption
یک روش خوشه بندی مبتنی بر شکل برای تشخیص الگوی مصرف برق مسکونی-2019 Pattern recognition of residential electricity consumption refers to discover different electricity consumption
patterns from electricity consumption data (ECD), which can provide valuable insights for
developing personalized marketing strategies, supporting targeted demand side management, and
improving energy utilization efficiency. To improve the efficiency and effectiveness of ECD analysis, we
proposed an improved K-means algorithm, in which principal component analysis (PCA) was used to
reduce the dimensions of smart meter time series data and the initial cluster centers were optimized.
3000 daily electricity consumption profiles (ECPs) of 1000 residents, obtained from the smart metering
electricity customer behavior trials of Irish, and 2000 yearly residential ECPs from Jiangsu Province,
China, were used in the experiments. The ECPs were divided into 7 and 4 clusters respectively based on
their ECPs, and the characteristics of each cluster were extracted. In addition, the changes of residential
electricity consumption are also reflected in the shape variation of ECPs. However, traditional similarity
measurements cannot find the shape similarity of ECPs. Therefore, a shape-based clustering method was
also proposed to group ECPs with similar shapes and the detailed algorithm procedures were provided.
The results showed that the shape-based clustering method can effectively find similar shapes and
identify typical electricity consumption patterns based on daily ECPs Keywords: Electricity consumption pattern | Shape-based clustering | Dynamic time wrapping | Smart meter data |
مقاله انگلیسی |
4 |
Motif-based association rule mining and clustering technique for determining energy usage patterns for smart meter data
روش استخراج و مجموعه خوشه بندی قانون مبتنی بر موتیف برای تعیین الگوهای مصرف انرژی برای داده های کنتور هوشمند-2019 Nowadays, smart energy meters are being used to record periodic electricity consumption. The real time data
produced by smart meters provide the detailed information about the electricity usage of a particular consumer.
In this paper, we propose a motif-based association rule mining and clustering technique for determining the
energy usage patterns for smart meter data. The association rules of motifs within a specific time window
characterizes behaviors of energy consumer. In particular, we focus on an extraction of the temporal information
of the smart meter. The process is based on the unique combination of Symbolic Aggregate approximation (SAX),
temporal motif discovery and association rule mining to detect the expected and unexpected patterns robustly.
Experiments on real world smart meter datasets justify that the proposed model discovers the useful routine
behavior of electricity energy consumers, which are helpful for electricity utility experts. Further, in this paper,
clustering on the motifs is performed which gives the different consumption behavior of consumers on different
days which can help distribution network operator (DNO) for electricity network modeling and management. In
future, we can form motif-based signature using the proposed approach for different applications such as
anomaly detection and dynamic detection of operating patterns. Keywords: Smart Meter | Association rule | Data analytics | Temporal data mining | Clustering Motif |
مقاله انگلیسی |
5 |
What’s in the box?! Towards explainable machine learning applied to non-residential building smart meter classification
جعبه چیست؟ به سمت کاربرد یادگیری ماشین قابل توضیح برای طبقه بندی کنتورهای هوشمند ساختمان غیر مسکونی-2019 Feature engineering and data-driven classification models are at the forefront of analysis of large temporal sensor data from the built environment. In previous effort s, temporal features were engineered from the whole building hourly electrical meter data from 507 non-residential buildings. These features fall within the three general categories of statistics, model, and pattern-based and can be used to identify various behavior in the structure of the whole building electrical meter data. In this paper, a deeper investiga- tion is made of exactly what types of behavior are most important in the context of two classification scenarios: the primary use of a building and the level of performance the building has when compared to its peers. The highly comparative time-series analysis (hctsa) toolkit is used to analyze the most im- portant temporal features for the classification of various building performance attributes. In the first analysis, a comparison is made to distinguish the behavior between university dormitories (70 buildings) and laboratories (95 buildings) as an example of interpreting the classification of the primary-use-type of a building. In the second analysis, a comparison of buildings with high (165 buildings) versus low (169 buildings) consumption is used to extract and understand the behavior that indicates the level of the energy performance of a building. These two case study examples provide a foundation for further ex- plainable machine learning techniques in both classification and prediction as applied to buildings. This effort is the first example of machine learning with an explicit focus on the interpretability of classifica- tion for smart meter data from non-residential buildings. Keywords: Interpretable machine learning | Explainable machine learning | Building performance analysis | Performance classification | Energy efficiency | Smart meter | Temporal feature engineering | Load clustering | Data science | Customer segmentation | Time-series analysis |
مقاله انگلیسی |
6 |
Deep ensemble learning based probabilistic load forecasting in smart grids
پیش بینی بار احتمالی مبتنی بر یادگیری گروه عمیق در شبکه های هوشمند-2019 With the availability of fine-grained smart meter data, there has been increasing interest in using this information for ecient and
reliable energy management. In particular, accurate probabilistic load forecasting for individual consumers is critical in determining
the uncertainties in future demand with the goal of improving smart grid reliability. Compared with the aggregate loads, individual
load profiles exhibit higher irregularity and volatility and thus less predictable. To address these challenges, a novel deep ensemble
learning based probabilistic load forecasting framework is proposed to quantify the load uncertainties of individual customers.
This framework employs the profiles of dierent customer groups integrated into the understanding of the task. Specifically,
customers are clustered into separate groups based on their profiles and multitask representation learning is employed on these
groups simultaneously. This leads to a better feature learning across groups. Case studies conducted on an open access dataset from
Ireland demonstrate the eectiveness and superiority of the proposed framework Keywords: Deep ensemble learning | multitask representation learning | probabilistic load forecasting | smart grid | customer profiles |
مقاله انگلیسی |
7 |
Time series grouping algorithm for load pattern recognition
الگوریتم گروه بندی سری زمانی برای تشخیص الگوی بار-2019 System analysis and real-time operations in power distribution utilities require an accurate but compact
load data model created on the basis of large number of consumers’ measurements modeled as highdimensional
time series. This paper proposes an algorithm for grouping time series with similar load
patterns and extracting characteristic representatives of loads from the obtained groups, resulting in
reduced load data model size. The proposed Time Series Grouping Algorithm combines dimensionality
reduction, both partitional and hierarchical clustering and cluster validation to group time series into an
optimal number of clusters based on simple parametric settings. The usefulness of the proposed
algorithm is proven in a case study implemented in R language. The case study was conducted on real
smart meter data from three distribution networks: one North American and the other two European.
Results of the case study confirm that the proposed solution achieves high cluster validity and short
execution time comparing to related algorithms. Therefore, the article’s main contribution is load pattern
recognition support convenient for applications in distribution management systems. Keywords: Time series | Load profiles | Machine learning | Pattern recognition | Clustering | Smart grid |
مقاله انگلیسی |
8 |
Prediction Method for Smart Meter Life Based On Big Data
روش پیش بینی برای زندگی هوشمند متر بر اساس داده های بزرگ-2018 In order to better investigate the working life of Smart Meter and discover in advance the possible faults existed in the same
batch, the operational data of Smart Meter in running state has to be analyzed so as to construct the prediction model of
Smart Meter life. Firstly, we collect all the data from the running smart meters regarding the operational faults, maintenance
management, and their application data from the Power Information Collection System; Secondly, we analyze the
operational data in running state; Lastly, according to the advantages and disadvantages of the three reliability prediction
methods (element stress method, reliability prediction method based on reliability test, and reliability prediction method
based on reliability verification), we can protract the model that can reflect the life degradation characteristics and life
prediction of smart meter.
Keywords: smart meter, reliability prediction technology, big data |
مقاله انگلیسی |
9 |
Multi-Granular Electricity Consumer Load Profiling for Smart Homes using a Scalable Big Data Algorithm
تولید گرانول الکتریکی مصرف کننده بار برای ساخت خانه های هوشمند با استفاده از یک الگوریتم داده های بزرگ مقیاس پذیر-2018 With rising electricity prices, there is a need to give consumers greater control over their energy
consumption. It is anticipated that such informed consumers in control of their consumption patterns
will contribute to reduced energy usage and thus a sustainable environment. Smart meter technology
in smart homes provides real-time information to customers through devices such as in-home displays
and web portals, and provide half-hourly consumption data to electricity distributors and retailers.
Such data enables the profiling of consumers making it possible to understand different life styles and
electricity usage behaviours to provide customised electricity billing. To obtain the anticipated benefit
from such highly granular and high frequency data, it is essential to have big data technologies which
can process such volumes of data in near real time. The research described in this paper focus on
addressing the key requirements of large volume data processing and making use of the highly
granular nature of the data. Adapting a new scalable algorithm introduced by the authors for big data
processing, this work demonstrates the practicality of processing large volumes of data at multiple
levels of granularity. The faster processing capacity makes it possible to continuously analyse
consumption data at frequent intervals as they are collected and at a highly granular level thus
providing a practical solution as a smart home application. The advantages of the technique is
demonstrated using electricity consumption data for 10,000 households for a year from an Australian
electricity retailer.
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مقاله انگلیسی |
10 |
Compression of smart meter big data_ A survey
فشرده سازی داده های بزرگ متریک هوشمند : یک مرور-2018 In recent years, the smart grid has attracted wide attention from around the world. Large scale data are collected
by sensors and measurement devices in a smart grid. Smart meters can record fine-grained information about
electricity consumption in near real-time, thus forming the smart meter big data. Smart meter big data has
provided new opportunities for electric load forecasting, anomaly detection, and demand side management.
However, the high-dimensional and massive smart meter big data not only creates great pressure on data
transmission lines, but also incur enormous storage costs on data centres. Therefore, to reduce the transmission
pressure and storage overhead, improve data mining efficiency, and thus fulfil the potential of smart meter big
data. This study presents a comprehensive study on the compression techniques for smart meter big data. The
development of smart grids and the characteristics and application challenges of electric power big data are first
introduced, followed by analysis of the characteristics and benefits of smart meter big data. Finally, this study
focuses on the potential data compression methods for smart meter big data, and discusses the evaluation
methods for smart meter big data compression.
Keywords: Smart grid ، Smart meter ، Energy big data ، Data compression |
مقاله انگلیسی |