7,787 research outputs found

    A novel feature set for low-voltage consumers, based on the temporal dependence of consumption and peak demands

    Get PDF
    This paper proposes a novel feature construction methodology aiming at both clustering yearly load profiles of low-voltage consumers, as well as investigating the stochastic nature of their peak demands. These load profiles describe the electricity consumption over a one-year period, allowing the study of seasonal dependence. The clustering of load curves has been extensively studied in literature, where clustering of daily or weekly load curves based on temporal features has received the most research attention. The proposed feature construction aims at generating a new set of variables that can be used in machine learning applications, stepping away from traditional, high dimensional, chronological feature sets. This paper presents a novel feature set based on two types of features: respectively the consumption time window on a daily and weekly basis, and the time of occurrence of peak demands. An analytic expression for the load duration curve is validated and leveraged in order to define the the region that has to be considered as peak demand region. The clustering results using the proposed set of features on a dataset of measured Flemish consumers at 15-min resolution are evaluated and interpreted, where special attention is given to the stochastic nature of the peak demands

    Predicting Electrical Power Consumption on Yearly Events for Substations: Algorithm Design and Performance Evaluations

    Get PDF
    Master's thesis in Information- and communication technology (IKT590)Accurate prediction of electricity usage is critical for grid companies in or-der to ensure reliable power supply for their customers. Many factors in-fluence usage patterns, but generally they consist of yearly-, weekly- and daily trends in addition to stochastic noise due to random user behaviour. Besides the above-mentioned cyclic trends, certain yearly events, i.e. events that take place once per year, can affect usage patterns significantly and thus may cause abnormally high or -low power consumption. Therefore, it is in the interest of grid companies to predict the consumption on such events so they can take measures in advance, if necessary. Much effort has been put into developing methods of improving forecasting accuracy through the use of time series clustering in conjunction with the actual prediction algorithm, but the methods’ ability to specifically improve the prediction of power consumption on yearly events has not yet been evaluated. In this the-sis, we are going to utilize machine learning algorithms to cluster electricity usage patterns and predict power consumption for yearly events based on real operational data at the substation level. More specifically, groups of similar usage profiles are formed by a clustering algorithm, and rather than training a prediction model on a single time series, a similar series from the same cluster is appended. In order to extend the prediction model’s training set in this manner, the appended time series is transformed to fit the scale of the initial time series. Our experiments reveal that combining similar time series, thereby introducing additional yearly events to the prediction model’s training set, can improve the accuracy of the load forecast on the event. This approach is also capable of compensating for missing events in the initial time series, when present in the appended-, similarly behaving time series

    A Hybrid Clustering and ClassiïŹcation Technique for Forecasting Short-Term Energy Consumption

    Get PDF
    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 efïŹcient parameters and with the use of CBA-ANN-SVM model, the rate of error has been minimized. After consulting with experts in the ïŹeld of power consumption and plotting daily power consumption for each week, this research showed that ofïŹcial 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 identiïŹed 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 ïŹnal model. The ïŹnal model has the beneïŹts from both models and the beneïŹts 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.1293

    Cluster Analysis and Model Comparison Using Smart Meter Data.

    Full text link
    Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values

    New methods for clustering district heating users based on consumption patterns

    Get PDF
    This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordUnderstanding energy users’ consumption patterns benefits both utility companies and consumers as it can support improving energy management and usage strategies. The rapid deployment of smart metering facilities has enabled the analysis of consumption patterns based on high-precision real usage data. This paper investigates data-driven unsupervised learning techniques to partition district heating users into separate clusters such that users in the same cluster possess similar consumption pattern. Taking into account the characteristics of heat usage, three new approaches of extracting pattern features from consumption data are proposed. Clustering algorithms with these features are executed on a real-world district heating consumption dataset. The results can reveal typical daily consumption patterns when the consumption linearly related to ambient temperature is removed. Users with heat usages that are highly imbalanced within a certain period of time or are highly consistent with the utility heat production load can also be grouped together. Our methods can facilitate gaining better knowledge regarding the behaviors of district heating users and hence can potentially be used to formulate new pricing and energy reduction solutions.European Commissio

    Generation of customer load profiles based on smart-metering time series, building-level data and aggregated measurements

    Get PDF
    Development of a load profile generator based on Markov chains. The model exploits a dataset of load profiles from smart metering. The model performs well in reproducing consumption patters of residential load profile

    Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data

    Get PDF
    Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.TU Berlin, Open-Access-Mittel - 201
    • 

    corecore