Comparison and clustering analysis of the daily electrical load in eight European countries

Ferraro, Pietro; Crisostomi, Emanuele; Tucci, Mauro; Raugi, Marco

doi:10.1016/j.epsr.2016.07.002

Cited by 30 publications

(14 citation statements)

References 17 publications

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“…In order to classify the group households precisely, the households in the community were clustered according to the peak consumption times and timetables of residents, such as the morning, noon, evening peak time, wake up time, and bedtime. In this paper, the adopted clustering method is fuzzy c-means clustering (FCM) [28][29][30]. Using the FCM algorithm, group households can be classified into different categories based on the consumption time information for each family.…”

Section: Forecasting Results For Group Householdmentioning

confidence: 99%

A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents

2018

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Abstract:The forecasting of the load profile of the domestic sector is an area of increased concern for the power grid as it appears in many applications, such as grid operations, demand side management, energy trading, and so forth. Accordingly, a bottom-up forecasting framework is presented in this paper based upon bottom level data about the electricity consumption of household appliances. In the proposed framework, a load profile for group households is obtained with a similar day extraction module, household behavior analysis module, and household behavior prediction module. Concretely, similar day extraction module is the core of the prediction and is employed to extract similar historical days by considering the external environmental and household internal influence factors on energy consumption. The household behavior analysis module is used to analyse and formulate the consumption behavior probability of appliances according to the statistical characteristics of appliances' switch state in historical similar days. Based on the former two modules, household behavior prediction module is responsible for the load profile of group households. Finally, a case study based on the measured data in a practical residential community is performed to illustrate the feasibility and effectiveness of the proposed bottom-up household load forecasting approach.

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Section: Forecasting Results For Group Householdmentioning

confidence: 99%

A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents

2018

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“…Since estimating the electricity demand becomes harder as the planning horizon increases, the predictions can be strongly influenced by several nonuniform variables such as electric consumption, temperature, air humidity, and socioeconomic aspects. Moreover, long-and regular-term time series make the problem more difficult to be technically managed and solved, as obtaining a computationally robust solution to act in real scenarios requires the integration of customized tuning approaches and non-linear models as a unified framework to properly work [15][16][17][18][19]. Therefore, in this paper, our main interest lies in designing well-behaved forecasters to assess and predict the electricity demand in Brazil for both long-and regular-term time series.Considering the recent advances in Machine Learning (ML) for electricity load forecasting, the literature offers a variety of approaches, most of them specifically designed to solve a particular case study of energy consumption.…”

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confidence: 99%

Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models

et al. 2020

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The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. In this study, the problem of forecasting the energy demand in the Brazilian Interconnected Power Grid was addressed, by gathering different energy-related datasets taken from public Brazilian agencies into a unified and open database, used to tune three machine learning models. In contrast to several works in the Brazilian context, which provide only annual/monthly load estimations, the learning approaches Random Forest, Gradient Boosting, and Support Vector Machines were trained and optimized as new ensemble-based predictors with parameter tuning to reach accurate daily/monthly forecasts. Moreover, a detailed and in-depth exploration of energy-related data as obtained from the Brazilian power grid is also given. As shown in the validation study, the tuned predictors were effective in producing very small forecasting errors under different evaluation scenarios.Techniques devoted to forecasting electricity demand aim at estimating the amount of energy needed, over a historical time series, for transmission and later consumption by others. Despite the adaptation of several machine learning models to properly address the problem [5-9], tracking the progressive use of the electricity is not a straightforward task in practice. In fact, the electricity dispatch is intrinsically related to the internal operations of the power systems such as the periodic scheduling of power generation in hydroelectric plants, the preventive maintenance of the generators, the reliability evaluation of power systems, etc. [10]. Moreover, the problem becomes even more challenging and especially interesting when one has to deal with the highly nonlinear tendency of power data, as it is mathematically modeled by highly-oscillating time series whose parameters can be affected by exogenous variables such as weather/ambient conditions [11] and economy-related factors [12].Formally, the problem of predicting the power demand on time series can be described as follows: given a time series of electric load X 1 , X 2 , . . . , X t , in which X i accounts for the historical energy load at the instant i, i = 1, 2, ..., t, the goal is to predict the quantity X (t+h) , where h establishes the forecast horizon [13,14]. Taxonomically speaking, this kind of prediction usually comprises three categories of planning horizons: (i) long-term (years/months); (ii) regular-term (days/weeks); and (iii) short-term (minutes/hours). Since estimating the electricity demand becomes harder as the planning horizon increases, the predictions can be strongly influenced by several nonuniform variables such as electric consumption, temperature, air humidity, and socioeconomic aspects. Moreover, long-and regular-term time series make the problem more difficult to be technically manag...

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“…Electrical Load (EL) forecasting is a fundamental and vital task for economically efficient operation and controlling of power systems [1][2][3][4][5]. It has often been employed for energy management, unit commitment and load dispatch [6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

Dong

Wang

et al. 2017

Energies

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Abstract:The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.

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Comparison and clustering analysis of the daily electrical load in eight European countries

Cited by 30 publications

References 17 publications

A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents

A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents

Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models

Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

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