27,001 research outputs found

    Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

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    Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Calavia Domínguez, L.; Carro Martínez, B.; Sanchez-Esguevillas, A.; Sanjuan, J.... (2013). Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment. Energies. 6(9):4489-4507. doi:10.3390/en6094489S4489450769Brooks, A., Lu, E., Reicher, D., Spirakis, C., & Weihl, B. (2010). Demand Dispatch. IEEE Power and Energy Magazine, 8(3), 20-29. doi:10.1109/mpe.2010.936349Chan, S. C., Tsui, K. M., Wu, H. C., Hou, Y., Wu, Y.-C., & Wu, F. (2012). Load/Price Forecasting and Managing Demand Response for Smart Grids: Methodologies and Challenges. IEEE Signal Processing Magazine, 29(5), 68-85. doi:10.1109/msp.2012.2186531Mohan Saini, L., & Kumar Soni, M. (2002). Artificial neural network-based peak load forecasting using conjugate gradient methods. IEEE Transactions on Power Systems, 17(3), 907-912. doi:10.1109/tpwrs.2002.800992Hyndman, R. J., & Fan, S. (2010). Density Forecasting for Long-Term Peak Electricity Demand. IEEE Transactions on Power Systems, 25(2), 1142-1153. doi:10.1109/tpwrs.2009.2036017McSharry, P. E., Bouwman, S., & Bloemhof, G. (2005). Probabilistic Forecasts of the Magnitude and Timing of Peak Electricity Demand. IEEE Transactions on Power Systems, 20(2), 1166-1172. doi:10.1109/tpwrs.2005.846071Amin-Naseri, M. R., & Soroush, A. R. (2008). Combined use of unsupervised and supervised learning for daily peak load forecasting. Energy Conversion and Management, 49(6), 1302-1308. doi:10.1016/j.enconman.2008.01.016Maksimovich, S. M., & Shiljkut, V. M. (2009). The Peak Load Forecasting Afterwards Its Intensive Reduction. IEEE Transactions on Power Delivery, 24(3), 1552-1559. doi:10.1109/tpwrd.2009.2014267Moazzami, M., Khodabakhshian, A., & Hooshmand, R. (2013). A new hybrid day-ahead peak load forecasting method for Iran’s National Grid. Applied Energy, 101, 489-501. doi:10.1016/j.apenergy.2012.06.009Hernández, L., Baladrón, C., Aguiar, J., Carro, B., & Sánchez-Esguevillas, A. (2012). Classification and Clustering of Electricity Demand Patterns in Industrial Parks. Energies, 5(12), 5215-5228. doi:10.3390/en5125215Hernandez, L., Baladrón, C., Aguiar, J., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2013). Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies, 6(3), 1385-1408. doi:10.3390/en6031385Razavi, S., & Tolson, B. A. (2011). A New Formulation for Feedforward Neural Networks. IEEE Transactions on Neural Networks, 22(10), 1588-1598. doi:10.1109/tnn.2011.2163169Hernández, L., Baladrón, C., Aguiar, J., Calavia, L., Carro, B., Sánchez-Esguevillas, A., … Lloret, J. (2013). Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks. Energies, 6(6), 2927-2948. doi:10.3390/en6062927Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A., Lloret, J., … Cook, D. (2013). A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants. IEEE Communications Magazine, 51(1), 106-113. doi:10.1109/mcom.2013.640044

    Towards An Enhanced Backpropagation Network for Short-Term Load Demand Forecasting

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    Artificial neural networks (ANNs) are ideal for the prediction and classification of non-linear relationships however they are also known for computational intensity and long training times especially when large data sets are used. A two-tiered approach combining data mining algorithms is proposed in order to enhance an artificial neural network’s performance when applied to a phenomenon exhibits predictable changes every calendar year such as that of electrical load demand. This approach is simulated using the French zonal load data for 2016 and 2017. The first tier performs clustering into seasons and classification into day-types. The second tier uses artificial neural networks to forecast 24-hour loads. The first tier results are the focus of this. The K-means algorithm is first applied to the morning slope feature of the data set and a comparison is then made between the Naïve Bayes algorithm and the k-Nearest Neighbors algorithm to determine the better classifier for this particular data set. The first tier results show that calendar-based clustering does not accurately reflect electrical load behavior. The results also show that k-Nearest Neighbors is the better classifier for this particular data set. It is expected that by optimizing the data set and reducing training time, the learning performance of ANN-based short-term load demand forecasting

    Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection

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    This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recastaccuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast horizons. We also find that machine learning methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This results shows the suitability of SVR for medium and long term forecasting.Peer ReviewedPostprint (published version

    Does money matter in inflation forecasting?.

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    This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation

    Mathematical Models for Natural Gas Forecasting

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    It is vital for natural gas Local Distribution Companies (LDCs) to forecast their customers\u27 natural gas demand accurately. A significant error on a single very cold day can cost the customers of the LDC millions of dollars. This paper looks at the financial implication of forecasting natural gas, the nature of natural gas forecasting, the factors that impact natural gas consumption, and describes a survey of mathematical techniques and practices used to model natural gas demand. Many of the techniques used in this paper currently are implemented in a software GasDayTM, which is currently used by 24 LDCs throughout the United States, forecasting about 20% of the total U.S. residential, commercial, and industrial consumption. Results of GasDay\u27sTM forecasting performance also is presented
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