Climate change and electricity demand in Brazil: A stochastic approach

Trotter, Ian Michael; Bolkesjø, Torjus Folsland; Féres, José Gustavo; Hollanda, Lavinia

doi:10.1016/j.energy.2016.02.120

Cited by 56 publications

(38 citation statements)

References 23 publications

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“…Climate change will affect both the energy demand and the supply, for example increasing the cooling demand and decreasing the heating demand [110] and intensifying extreme events [15], threatening the security of generation, transmission, and distribution infrastructure [111]. A number of studies have focused on the impact of climate change at both national [112], [113] and continental scale [114], [115].…”

Section: Section 4: Assessment Of the Urban Built Environment Urban mentioning

confidence: 99%

A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities

Mauree

Naboni

Coccolo

et al. 2019

Renewable and Sustainable Energy Reviews

167

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Reviewed assessment methods for the urban environment • Critically analysed papers working on urban climate and energy demand, outdoor thermal comfort and the urban energy systems. • Demonstrated the links between the processes • An integrated workflow is proposed for assessment of the urban environment. AbstractThe current climate change is calling for a drastic reduction of energy demand as well as of greenhouse gases. Besides this, cities also need to adapt to face the challenges related to climate change. Cities, with their complex urban texture and fabric, can be represented as a diverse ecosystem that does not have a clear and defined boundary. Multiple software tools that have been developed, in recent years, for assessment of urban climate, building energy demand, the outdoor thermal comfort and the energy systems. In this review, we, however, noted that these tools often address only one or two of these urban planning aspects. There is nonetheless an intricate link between them. For instance, the outdoor comfort assessment has shown that there is a strong link between biometeorology and architecture and urban climate. Additionally, to address the challenges of the energy transition, there will be a convergence of the energy needs in the future with an energy nexus regrouping the energy demand of urban areas. It is also highlighted that the uncertainty related to future climatic data makes urban adaptation and mitigation strategies complex to implement and to design given the lack of a comprehensive framework. We thus conclude by suggesting the need for a holistic interface to take into account this multi-dimensional problem. With the help of such a platform, a positive loop in urban design can be initiated leading to the development of low carbon cities and/or with the use of blue and green infrastructure to have a positive impact on the mitigation and adaptation strategies.

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Section: Section 4: Assessment Of the Urban Built Environment Urban mentioning

confidence: 99%

A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities

Mauree

Naboni

Coccolo

et al. 2019

Renewable and Sustainable Energy Reviews

167

View full text Add to dashboard Cite

show abstract

“…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). Similarly, a year-by-year estimation for the electric demand in Brazil was also carried out by Trotter et al [29], by modeling uncertainty in the estimates of weather variables. Their approach relies on basic features such as population size and national income together with the electricity demand so that a multiple linear regression model is obtained to yield annual forecasts.…”

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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“…Lastly, they utilized Artificial Intelligent (AI) methods, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), to estimate the sensitivity of the initial value and to perform the long-term load demand forecast. Trotter et al, [6] presented a stochastic approach to forecast the climate change and long-term electricity demand in Brazil. They applied multiple linear regression model to calibrate electricity demand data series and forecasted the data series using the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Electricity Load Demand Forecast using Fast Ensemble-Decomposed Model

Akrom¹,

Ismail²

2018

JST

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Electricity load demand forecasting is a complex process since the pattern of electricity load demand data sets varies. To overcome this problem, a fast ensemble-decomposed model was proposed in this work. Firstly, two data sets of electricity load demand, which are electricity consumption and electricity production, were decomposed into two Intrinsic Mode Functions (IMFs). Secondly, the different values of ensemble trials are employed into fast ensemble-decomposed model. Then, the second IMF was used as the intrinsic prediction trend for the actual electricity load demand data sets. Lastly, the second IMF was compared with the actual electricity load demand time series data and the intrinsic prediction trend of the second IMF was forecasted. Simulation results revealed that the FED model better than the ARIMA and ANN methods and different values of ensembles trials do effect forecast accuracy.

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Climate change and electricity demand in Brazil: A stochastic approach

Cited by 56 publications

References 23 publications

A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities

A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities

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

Electricity Load Demand Forecast using Fast Ensemble-Decomposed Model

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