Load and electricity prices forecasting using Generalized Regression Neural Networks

Paulos, Jose Pedro; Fidalgo, José Nuno

doi:10.1109/sest.2018.8495726

2018 International Conference on Smart Energy Systems and Technologies (SEST) 2018

DOI: 10.1109/sest.2018.8495726

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Load and electricity prices forecasting using Generalized Regression Neural Networks

Jose Pedro Paulos

José Nuno Fidalgo

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2020

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

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

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Load and electricity prices forecasting using Generalized Regression Neural Networks

Cited by 1 publication

References 11 publications

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

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

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