107 research outputs found

    Método para la Predicción de Demanda Mensual de Electricidad en Colombia utilizando Análisis Wavelet y Modelos Auto-regresivos No Lineales

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    This paper proposes a monthly electricity forecast method for the National Interconnected System (SIN) of Colombia. The method preprocesses the time series using a Multiresolution Analysis (MRA) with Discrete Wavelet Transform (DWT); a study for the selection of the mother wavelet and her order, as well as the level decomposition was carried out. Given that original series follows a non-linear behaviour, a neural nonlinear autoregressive (NAR) model was used. The prediction was obtained by adding the forecast trend with the estimated obtained by the residual series combined with further components extracted from preprocessing.A bibliographic review of studies conducted internationally and in Colombia is included, in addition to references to investigations made with wavelet transform applied to electric energy prediction and studies reporting the use of NAR in predictionEn este artículo se propone un método para la predicción mensual de la demanda en el Sistema Interconectado Nacional Eléctrico de Colombia. El método realiza preprocesamiento de la serie de tiempo utilizando un análisis multiresolución mediante tranformada wavelet discreta; se presenta un estudio para la selección de la wavelet madre y su orden, asi como del nivel de descomposición. Dado que originalmente la serie tiene comportamiento no lineal, se utilizó igualmente un modelo no lineal autoregresivo. La predicción se obtiene añadiendo a la tendencia, el estimado obtenido con el residual de la serie combinado con otros componentes extraídos durante el preproceamiento.Se incluye una revisión bibliográfica de investigaciones realizadas internacionalmente y en Colombia en relación a la aplicación de la transformada wavelet y el modelo autoregresivo no lineal a la predicción de energía eléctrica

    An Overview of Forecasting Methods for Monthly Electricity Consumption

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    Mid-term electricity consumption forecasting is analysed in this paper. Forecasting of electricity consumption is regression problem that can be defined as using previous consumption of an individual or a group with the goal of calculation of future consumption using some mathematical or statistical approach. The purpose of this prediction is multi beneficial to the stakeholders in the energy community, since this information can affect production, sales and supply. The Different methods are considered with the main goal to determine the best forecasting model. Considered methods include Box-Jenkins autoregressive integrated moving average models, state-space models and exponential smoothing, and machine learning methods including neural networks. An additional objective of the conducted research was to determine if modern methods like machine learning are equally precise in forecasting mid-term electricity consumption when compared to traditional time series methods. The performances of forecasting models are evaluated on the monthly electricity consumption data obtained using real billing software owned by the Distribution System Operator in Bosnia and Herzegovina. Mean absolute percentage error is selected as a measure of prediction accuracy of forecasting methods. Every forecasting method is implemented and tested using the R language, while data is collected from Data Warehouse in the form of total monthly consumption. The efficiency of presented solution will also be discussed after presentation of the results

    Holt-Winters Forecasting for Brazilian Natural Gas Production

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    Nowadays, the market for natural gas production and its use as a source of energy supply has been growing substantially in Brazil. However, the use of tools that assist the industry in the management of production can be essential for the strategic decision-making process. In this intuit, this work aims to evaluate the formulation of Holt Winter\u27s additive and multiplicative time series to forecast Brazilian natural gas production. A comparison between the models and their forecast play a vital role for policymakers in the strategic plan, and the models estimated production values ​​for the year 2018 based on the information contained in the interval between 2010 and 2017. Therefore, It was verified that the multiplicative method had a good performance so that we can conclude this formulation is ideal for such an application since all the predicted results by this model showed greater accuracy within the 95% confidence interval

    Forecasting methods in energy planning models

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    Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs

    Avances recientes en la predicción de la demanda de electricidad usando modelos no lineales

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    La predicción de la demanda es un problema de gran importancia para el sector eléctrico, ya que a partir de sus resultados, los agentes del mercado de energía toman las decisiones más adecuadas para su labor. En este artículo se presenta un análisis de las técnicas y modelos más usados en el pronóstico de la demanda de electricidad y la problemática o dificultades a las que se enfrentan los investigadores al momento de realizar un pronóstico. El análisis muestra que las técnicas más usadas son los modelos ARIMA y las redes neuronales artificiales. Sin embargo, se encontró poca claridad sobre cuál modelo es más adecuado y en qué casos, adicionalmente, los estudios no presentan una recomendación específica para desarrollar modelos de pronóstico de demanda, específicamente en el caso colombiano. Finalmente, se propone realizar un estudio sistemático con el fi n de determinar los modelos más adecuados para predicción de demanda para el caso colombiano

    Forecasting The Italian Day-Ahead Electricity Price Using Bootstrap Aggregation Method

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    Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of the energy sector on a global scale since the late 1990s. Given these facts, this paper is an attempt to establish and demonstrate a precision based applicable forecasting model for wholesale electricity prices with respect to the Italian power market on an hourly basis. Artificial intelligence models such as neural networks and bagged regression trees are utilized, although they are rarely used to forecast electricity prices. After model calibration, bagged regression trees with exogenous variables comprised the final model. The selected model outperformed neural network and bagged regression with a single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies. We also confirm certain theoretical specifications of the model. As a policy tool, this model could be used by energy traders, transmission system operators and energy regulators for an enhanced decision-making process
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