Long term electricity consumption forecast in Brazil: A fuzzy logic approach

Torrini, Fabiano Castro; Souza, Reinaldo Castro; Oliveira, Fernando Luiz Cyrino; Pessanha, José Francisco Moreira

doi:10.1016/j.seps.2015.12.002

Cited by 67 publications

(24 citation statements)

References 4 publications

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“…Deka (2016) compared five different forecasting technologies using economic and demographic factors to simulate US energy needs with in-depth discussion [16]. Torrini (2016) proposed a fuzzy logic approach to extract rules from input variables and to provide Brazil's long-term annual electricity demand forecast [17]. Philip (2012) used ARDL and PAM to measure the short-term and long-term influencing factors of energy consumption in Ghana and forecasted Ghana's energy consumption in 2020 [18].…”

Section: Energy Consumption Forecastmentioning

confidence: 99%

Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network

Liu

Bielefield

et al. 2017

Energies

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Abstract:The forecasting of energy consumption in China is a key requirement for achieving national energy security and energy planning. In this study, multi-variable linear regression (MLR) and support vector regression (SVR) were utilized with a gated recurrent unit (GRU) artificial neural network of Chinese energy to establish a forecasting model. The derived model was validated through four economic variables; the gross domestic product (GDP), population, imports, and exports. The performance of various forecasting models was assessed via MAPE and RMSE, and three scenarios were configured based on different sources of variable data. In predicting Chinese energy consumption from 2015 to 2021, results from the established GRU model of the highest predictive accuracy showed that Chinese energy consumption would be likely to fluctuate from 2954.04 Mtoe to 5618.67 Mtoe in 2021.

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Section: Energy Consumption Forecastmentioning

confidence: 99%

Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network

Liu

Bielefield

et al. 2017

Energies

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“…Despite predicting consumption until 2050, their forecasts were only performed annually, giving no details for the demanded energy during the months. Another long-term forecasting study was conducted by Torrini et al [28]. The authors used a fuzzy logic-based methodology, which was calibrated with GDP and PGR indices, and compared their results with official projections for the sector, as provided by the Brazilian Energy Research Office (EPE -Empresa de Pesquisa Energética, in Portuguese: http://www.epe.gov.br/en).…”

Section: Introductionmentioning

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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“…Classical methods for long-term system load forecasting are mainly in three categories: time series models [2][3][4][5], correlation models [6][7][8][9] and artificial intelligence models [9][10][11][12][13]. Time series models forecast the future load based on the historical load data, so the underlying assumption is that the future load will follow the same trend as its past.…”

Section: Introductionmentioning

confidence: 99%

Long-term system load forecasting based on data-driven linear clustering method

Han

Yan

2017

J. Mod. Power Syst. Clean Energy

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In this paper, a data-driven linear clustering (DLC) method is proposed to solve the long-term system load forecasting problem caused by load fluctuation in some developed cities. A large substation load dataset with annual interval is utilized and firstly preprocessed by the proposed linear clustering method to prepare for modelling. Then optimal autoregressive integrated moving average (ARIMA) models are constructed for the sum series of each obtained cluster to forecast their respective future load. Finally, the system load forecasting result is obtained by summing up all the ARIMA forecasts. From error analysis and application results, it is both theoretically and practically proved that the proposed DLC method can reduce random forecasting errors while guaranteeing modelling accuracy, so that a more stable and precise system load forecasting result can be obtained.

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Long term electricity consumption forecast in Brazil: A fuzzy logic approach

Cited by 67 publications

References 4 publications

Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network

Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network

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

Long-term system load forecasting based on data-driven linear clustering method

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