The application of seasonal latent variable in forecasting electricity demand as an alternative method

Sümer, Kutluk Kağan; Göktaş, Özlem; Hepsağ, Aycan

doi:10.1016/j.enpol.2008.11.014

Cited by 44 publications

(15 citation statements)

References 22 publications

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“…Artificial and computational intelligence methods, such as neural networks and support vector regression methods were also indicated to have applications. Sumer et al [8] developed ARIMA, SARIMA and regression models with a latent seasonal variable in forecasting electricity demand. Taylor [9] used minute-byminute data on British electricity demand to evaluate 10-30 minutes ahead prediction methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting energy trends and peak usage at the University of Virginia

Heylman

Kim

Wang

2015

2015 Systems and Information Engineering Design Symposium

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Forecasting energy trends, especially peak usage, is a valuable and necessary part of energy management. Accurate prediction allows for the control and alleviation of overuse during peak times with the implementation of energy efficiencies. Using hourly kilowatt data from over 200 buildings on the University of Virginia's campus this paper examines the most effective techniques for developing both individual building and overall grid use energy models with a specific focus on predetermining peak usage points. This paper proposes that the expectation-maximization algorithm within the state-space framework is the most effective method for smoothing missing values, a common occurrence when working with metered energy data. Next, this paper covers two separate methods for creating forecasting models. The first, a linear model, was found to be most successful in predicting campuswide energy usage, with the inclusion of features of temperature, humidity, school session, and three temporal variables. The second method, found to be most successful when forecasting short term individual building energy use, uses a seasonal autoregressive integrated moving average (SARIMA) model. Finally this paper delves into the intricacies involved in clustering buildings based on their energy usage trends rather than building use, and the implications that arise from such a process. The conclusions made in this paper can be rescaled and applied to larger energy systems outside the university setting.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting energy trends and peak usage at the University of Virginia

Heylman

Kim

Wang

2015

2015 Systems and Information Engineering Design Symposium

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“…In recent studies, Autoregressive Integrated Moving Average (ARIMA) technique is frequently used as a forecasting tool [7,8,9,10]. Ceylan and Ozturk (2004) used a Genetic Algorithm (GA) based model for estimating energy demand [11].…”

Section: Introductionmentioning

confidence: 99%

Modelling Turkey's Energy Consumption Based on Artificial Neural Network

Küyük

Kayikci

Özkan

et al. 2010

2010 Ninth International Conference on Machine Learning and Applications

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Energy plays a fundamental role in an economy. Turkey has the world's 15th largest GDP-Purchasing power parity and 17th largest Nominal GDP. Economists and political scientists classify Turkey as a newly industrialized country. In this study, an alternative model for Turkey's energy consumption is proposed for the time between 1980 and 2004. Artificial neural network based model (ANN) is preferred as a forecasting tool. Gross domestic product (GDP), which is based on purchasing power parity, industrial production index and total population are utilized in the model. It is found that the energy consumption has direct relations with the Industrial Production Index. Moreover, population and GDP has causality effects.

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“…ARIMA, ETS and TBATS are parametric forecasting techniques whilst HW, NN and SSA are nonparametric forecasting techniques. ARIMA and HW have been used in the past and recently for forecasting energy data (see, for example [6,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]) whilst applications of NN models for energy forecasting can be found in [27][28][29][30][31]. In addition, ARIMA, ETS, HW and NN models are proven forecasting techniques which are adopted globally in many different fields, and thus enables a meaningful comparison whilst being suitable benchmarks.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the effectiveness of parametric and nonparametric energy consumption forecasts for a developing country

Silva¹,

Rajapaksa²

2014

Int. J. Energy Stat.

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This paper seeks to analyse and evaluate Sri Lanka's energy consumption forecasts, more specifically, electricity, petroleum, coal and renewable electricity consumption using a variety of parametric and nonparametric forecasting techniques. The Sri Lankan economy is emerging following the end of a prolonged civil war, and thus this topic is opportune as accurate forecasts of energy requirements are indispensable for sustaining the ongoing rapid economic expansion. We also consider evaluating the appropriateness of parametric and nonparametric energy forecasting methods for Sri Lanka. In addition, this paper marks the introductory application of SSA for forecasting in Sri Lanka, and the results from SSA are compared against the popular benchmarks of ARIMA, ETS, HW, TBATS, and NN. We find statistically significant evidence proving that the SSA model outperforms ETS and HW at forecasting renewable electricity consumption, and also, that the SSA model outperforms ARIMA and TBATS models at forecasting coal consumption. On average, the SSA model is found to be best for energy consumption forecasting in Sri Lanka whilst the Neural Networks model is second best

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The application of seasonal latent variable in forecasting electricity demand as an alternative method

Cited by 44 publications

References 22 publications

Forecasting energy trends and peak usage at the University of Virginia

Forecasting energy trends and peak usage at the University of Virginia

Modelling Turkey's Energy Consumption Based on Artificial Neural Network

Evaluating the effectiveness of parametric and nonparametric energy consumption forecasts for a developing country

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