Forecasting methods in energy planning models

Debnath, Kumar Biswajit; Mourshed, Monjur

doi:10.1016/j.rser.2018.02.002

Cited by 271 publications

(130 citation statements)

References 462 publications

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“…With reference to projections, the recent review by Debnath and Mourshed (2018) [58] illustrates a variety of available methods to forecast input data for parameters. They found that most forecasting methods are used to project energy demand and electrical load.…”

Section: Limitations Of a Deterministic Uncertainty Analysismentioning

confidence: 99%

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Dreier¹,

Howells²

2019

Energies

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Recent open-data movements give access to large datasets derived from real-world observations. This data can be utilized to enhance energy systems modeling in terms of heterogeneity, confidence, and transparency. Furthermore, it allows to shift away from the common practice of considering average values towards probability distributions. In turn, heterogeneity and randomness of the real-world can be captured that are usually found in large samples of real-world data. This paper presents a methodological framework for an empirical deterministic–stochastic modeling approach to utilize large real-world datasets in long-term energy systems modeling. A new software system—OSeMOSYS-PuLP—was developed and is available now.It adds the feature of Monte Carlo simulations to the existing open-source energy modeling system (the OSeMOSYS modeling framework). An application example is given, in which the initial application example of OSeMOSYS is used and modified to include real-world operation data from a public bus transport system.

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Section: Limitations Of a Deterministic Uncertainty Analysismentioning

confidence: 99%

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Dreier¹,

Howells²

2019

Energies

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“…There are still one fifth of the population of the earth who do not have electricity in their living needs [1] this matter is of concern to the world government through United Nation efforts to achieve sustainable development goals (SDGs) in the energy sector. Various efforts at the global level continue to be made in meeting the demand and supply of energy in achieving national energy security each country through [2]- [4] china, Indonesia, Pakistan, Vietnam [5]- [8]. The new paradigm of the Indonesian government relating to the supply and demand of national energy, namely energy as development capital has emphasized the use of renewable energy (RE) as optimal as possible for the welfare of society.…”

Section: Introductionmentioning

confidence: 99%

Evaluation Of Indonesia's National Energy Policy: A Regional Dilemma In Indonesia Through Simulation Approach

Amheka¹,

Tanesab²,

Thies³

et al. 2019

Proceedings of the Proceedings of the 1st International Conference on Engineering, Science, and Commerce, ICESC 2019, 18-19 Oct

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The target of Indonesia's national primary energy supply in 2025 and 2050 are estimated to be at least 400 MTOE and 1000 MTOE accumulated from a total of 34 Provinces in Indonesia to fill the target. At the same years, target for per capita primary energy utilization of 1.4 TOE and 3.2 TOE and per capita electricity utilization of at least 2,500kWh and 7,000kWh. This study aims to determine the magnitude of the achievement targets of national energy policies from several provinces in Indonesia by taking samples, namely the Nusa Tenggara Timur, West Java, and Central Java Provinces. The quantitative approach for the year 2010 to 2015 data periods of the NTT Province have taken by considering parameters, among others the economic structure, environmental conditions, and specific energy conditions. All data are processed through LEAP assistance. The study results have shown a dilemma in achieving national energy policy targets represented by three sample provinces.

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“…And the method can identify relevant variables for developing the forecasting model [13]. It discussed the effects of various models in energy planning and forecasting, with emphasis on Sustainability 2018, 10, 3282 2 of 22 the application of ANN in the field of load forecasting [14,15]. It introduced different power load forecasting models and combined regression model with machine learning model to forecast the power load of commercial buildings [16].…”

Section: Introductionmentioning

confidence: 99%

Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory

Sun

Huang

et al. 2018

Sustainability

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Accurate load forecasting can help alleviate the impact of renewable-energy access to the network, facilitate the power plants to arrange unit maintenance and encourage the power broker companies to develop a reasonable quotation plan. However, the traditional prediction methods are insufficient for the analysis of load sequence fluctuations. The economic variables are not introduced into the input variable selection and the redundant information interferes with the final prediction results. In this paper, a set of the ensemble empirical mode is used to decompose the electricity consumption sequence. Appropriate economic variables are as selected as model input for each decomposition sequence to model separately according to its characteristics. Then the models are constructed by selecting the optimal parameters in the random forest. Finally, the result of the component prediction is reconstituted. Compared with random forest, support vector machine and seasonal naïve method, the example results show that the prediction accuracy of the model is better than that of the contrast models. The validity and feasibility of the method in the monthly load forecasting is verified.

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Forecasting methods in energy planning models

Cited by 271 publications

References 462 publications

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Evaluation Of Indonesia's National Energy Policy: A Regional Dilemma In Indonesia Through Simulation Approach

Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory

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