Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting

Load forecasting is critical for power system operation and market planning.With the increased penetration of renewable energy and the massive consumption of electric energy, improving load forecasting accuracy has become a dif�cult task. Recently, it was demonstrated that deep learning models perform well for short-term load forecasting (STLF). However, prior research has demonstrated that the hybrid deep learning model outperforms the single model. We propose a hybrid neural network in this article that combines elements of a convolutional neural network (1D-CNN) and a long short memory network (LSTM) in novel ways. Multiple independent 1D-CNNs are used to extract load, calendar, and weather features from the proposed hybrid model, while LSTM is used to learn time patterns. This architecture is referred to as a CNN-LSTM network with multiple heads (MCNN-LSTM). To demonstrate the proposed hybrid deep learning model's superior performance, the proposed method is applied to Ireland's load data for single-step and multi-step load forecasting. In comparison to the widely used CNN-LSTM hybrid model, the proposed model improved single-step prediction by 16.73% and 24-step load prediction by 20.33%. Additionally, we use the Maine dataset to verify the proposed model's generalizability

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc.), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN) that performs Short-Term Load Forecasting (STLF). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Carro Martínez, 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/en6031385S1385140863Booklets European Comission. Your Guide to the Lisbon Treaty 2009http://ec.europa.eu/publications/booklets/others/84/en.pdfHernandez, 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.2013.6400446http://ec.europa.eu/energy/technology/set_plan/set_plan_en.htmFUTURED—Spanish Technological Platform for Energy Grids Home Pagehttp://www.futured.es/European Technology Platform for Electricity Networks of the Future—SmartGrids ETP Home Pagehttp://www.smartgrids.eu/Kim, H.-M., Kinoshita, T., & Shin, M.-C. (2010). A Multiagent System for Autonomous Operation of Islanded Microgrids Based on a Power Market Environment. Energies, 3(12), 1972-1990. doi:10.3390/en3121972Kim, H.-M., Lim, Y., & Kinoshita, T. (2012). An Intelligent Multiagent System for Autonomous Microgrid Operation. Energies, 5(9), 3347-3362. doi:10.3390/en5093347Xiao, Z., Li, T., Huang, M., Shi, J., Yang, J., Yu, J., & Wu, W. (2010). Hierarchical MAS Based Control Strategy for Microgrid. Energies, 3(9), 1622-1638. doi:10.3390/en3091622Hong, Y.-Y., & Chou, J.-H. (2012). Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks. Energies, 5(7), 2578-2593. doi:10.3390/en5072578Douglas, A. P., Breipohl, A. M., Lee, F. N., & Adapa, R. (1998). The impacts of temperature forecast uncertainty on Bayesian load forecasting. IEEE Transactions on Power Systems, 13(4), 1507-1513. doi:10.1109/59.736298Sadownik, R., & Barbosa, E. P. (1999). Short-term forecasting of industrial electricity consumption in Brazil. Journal of Forecasting, 18(3), 215-224. doi:10.1002/(sici)1099-131x(199905)18:33.0.co;2-bHuang, S. R. (1997). Short-term load forecasting using threshold autoregressive models. IEE Proceedings - Generation, Transmission and Distribution, 144(5), 477. doi:10.1049/ip-gtd:19971144Infield, D. G., & Hill, D. C. (1998). Optimal smoothing for trend removal in short term electricity demand forecasting. IEEE Transactions on Power Systems, 13(3), 1115-1120. doi:10.1109/59.709108Sargunaraj, S., Sen Gupta, D. P., & Devi, S. (1997). Short-term load forecasting for demand side management. IEE Proceedings - Generation, Transmission and Distribution, 144(1), 68. doi:10.1049/ip-gtd:19970599Hong-Tzer Yang, & Chao-Ming Huang. (1998). A new short-term load forecasting approach using self-organizing fuzzy ARMAX models. IEEE Transactions on Power Systems, 13(1), 217-225. doi:10.1109/59.651639Hong-Tzer Yang, Chao-Ming Huang, & Ching-Lien Huang. (1996). Identification of ARMAX model for short term load forecasting: an evolutionary programming approach. IEEE Transactions on Power Systems, 11(1), 403-408. doi:10.1109/59.486125Yu, Z. (1996). A temperature match based optimization method for daily load prediction considering DLC effect. IEEE Transactions on Power Systems, 11(2), 728-733. doi:10.1109/59.496146Charytoniuk, W., Chen, M. S., & Van Olinda, P. (1998). Nonparametric regression based short-term load forecasting. IEEE Transactions on Power Systems, 13(3), 725-730. doi:10.1109/59.708572Taylor, J. W., & Majithia, S. (2000). Using combined forecasts with changing weights for electricity demand profiling. Journal of the Operational Research Society, 51(1), 72-82. doi:10.1057/palgrave.jors.2600856Ramanathan, R., Engle, R., Granger, C. W. J., Vahid-Araghi, F., & Brace, C. (1997). Short-run forecasts of electricity loads and peaks. International Journal of Forecasting, 13(2), 161-174. doi:10.1016/s0169-2070(97)00015-0Soliman, S. A., Persaud, S., El-Nagar, K., & El-Hawary, M. E. (1997). Application of least absolute value parameter estimation based on linear programming to short-term load forecasting. International Journal of Electrical Power & Energy Systems, 19(3), 209-216. doi:10.1016/s0142-0615(96)00048-8Hyde, O., & Hodnett, P. F. (1997). An adaptable automated procedure for short-term electricity load forecasting. IEEE Transactions on Power Systems, 12(1), 84-94. doi:10.1109/59.574927Ku-Long Ho, Yuan-Yih Hsu, Chuan-Fu Chen, Tzong-En Lee, Chih-Chien Liang, Tsau-Shin Lai, & Kung-Keng Chen. (1990). Short term load forecasting of Taiwan power system using a knowledge-based expert system. IEEE Transactions on Power Systems, 5(4), 1214-1221. doi:10.1109/59.99372Rahman, S., & Hazim, O. (1993). A generalized knowledge-based short-term load-forecasting technique. IEEE Transactions on Power Systems, 8(2), 508-514. doi:10.1109/59.260833Mori, H., & Kobayashi, H. (1996). Optimal fuzzy inference for short-term load forecasting. IEEE Transactions on Power Systems, 11(1), 390-396. doi:10.1109/59.486123Bakirtzis, A. G., Theocharis, J. B., Kiartzis, S. J., & Satsios, K. J. (1995). Short term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems, 10(3), 1518-1524. doi:10.1109/59.466494Papadakis, S. E., Theocharis, J. B., Kiartzis, S. J., & Bakirtzis, A. G. (1998). A novel approach to short-term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems, 13(2), 480-492. doi:10.1109/59.667372Elman, J. L. (1990). Finding Structure in Time. Cognitive Science, 14(2), 179-211. doi:10.1207/s15516709cog1402_1Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7(2-3), 195-225. doi:10.1007/bf00114844Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464-1480. doi:10.1109/5.58325Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., & Hernández, L. (2012). Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors. Sensors, 12(2), 1468-1481. doi:10.3390/s120201468Martínez-Martínez, V., Baladrón, C., Gomez-Gil, J., Ruiz-Ruiz, G., Navas-Gracia, L. M., Aguiar, J. M., & Carro, B. (2012). Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks. Sensors, 12(10), 14004-14021. doi:10.3390/s121014004Park, D. C., El-Sharkawi, M. A., Marks, R. J., Atlas, L. E., & Damborg, M. J. (1991). Electric load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 6(2), 442-449. doi:10.1109/59.76685Ho, K. L., Hsu, Y.-Y., & Yang, C.-C. (1992). Short term load forecasting using a multilayer neural network with an adaptive learning algorithm. IEEE Transactions on Power Systems, 7(1), 141-149. doi:10.1109/59.141697Drezga, I., & Rahman, S. (1998). Input variable selection for ANN-based short-term load forecasting. IEEE Transactions on Power Systems, 13(4), 1238-1244. doi:10.1109/59.736244Drezga, I., & Rahman, S. (1999). Short-term load forecasting with local ANN predictors. IEEE Transactions on Power Systems, 14(3), 844-850. doi:10.1109/59.780894Lee, K. Y., Cha, Y. T., & Park, J. H. (1992). Short-term load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 7(1), 124-132. doi:10.1109/59.141695Lu, C.-N., Wu, H.-T., & Vemuri, S. (1993). Neural network based short term load forecasting. IEEE Transactions on Power Systems, 8(1), 336-342. doi:10.1109/59.221223Papalexopoulos, A. D., Shangyou Hao, & Tie-Mao Peng. (1994). An implementation of a neural network based load forecasting model for the EMS. IEEE Transactions on Power Systems, 9(4), 1956-1962. doi:10.1109/59.331456Bakirtzls, A. G., Petridls, V., Klartzis, S. J., Alexladls, M. C., & Malssls, A. H. (1996). A neural network short term load forecasting model for the Greek power system. IEEE Transactions on Power Systems, 11(2), 858-863. doi:10.1109/59.496166AlFuhaid, A. S., El-Sayed, M. A., & Mahmoud, M. S. (1997). Cascaded artificial neural networks for short-term load forecasting. IEEE Transactions on Power Systems, 12(4), 1524-1529. doi:10.1109/59.627852Lamedica, R., Prudenzi, A., Sforna, M., Caciotta, M., & Cencellli, V. O. (1996). A neural network based technique for short-term forecasting of anomalous load periods. IEEE Transactions on Power Systems, 11(4), 1749-1756. doi:10.1109/59.544638Srinivasan, D. (1994). Forecasting daily load curves using a hybrid fuzzy-neural approach. IEE Proceedings - Generation, Transmission and Distribution, 141(6), 561. doi:10.1049/ip-gtd:19941288Kwang-Ho Kim, Jong-Keun Park, Kab-Ju Hwang, & Sung-Hak Kim. (1995). Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems. IEEE Transactions on Power Systems, 10(3), 1534-1539. doi:10.1109/59.466492Daneshdoost, M., Lotfalian, M., Bumroonggit, G., & Ngoy, J. P. (1998). Neural network with fuzzy set-based classification for short-term load forecasting. IEEE Transactions on Power Systems, 13(4), 1386-1391. doi:10.1109/59.736281Senjyu, T., Mandal, P., Uezato, K., & Funabashi, T. (2005). Next Day Load Curve Forecasting Using Hybrid Correction Method. IEEE Transactions on Power Systems, 20(1), 102-109. doi:10.1109/tpwrs.2004.831256Hsu, Y.-Y., & Yang, C.-C. (1991). Design of artificial neural networks for short-term load forecasting. Part 2: Multilayer feedforward networks for peak load and valley load forecasting. IEE Proceedings C Generation, Transmission and Distribution, 138(5), 414. doi:10.1049/ip-c.1991.0052Rejc, M., & Pantos, M. (2011). Short-Term Transmission-Loss Forecast for the Slovenian Transmission Power System Based on a Fuzzy-Logic Decision Approach. IEEE Transactions on Power Systems, 26(3), 1511-1521. doi:10.1109/tpwrs.2010.2096829Wang, Y., Xia, Q., & Kang, C. (2011). Secondary Forecasting Based on Deviation Analysis for Short-Term Load Forecasting. IEEE Transactions on Power Systems, 26(2), 500-507. doi:10.1109/tpwrs.2010.2052638Kebriaei, H., Araabi, B. N., & Rahimi-Kian, A. (2011). Short-Term Load Forecasting With a New Nonsymmetric Penalty Function. IEEE Transactions on Power Systems, 26(4), 1817-1825. doi:10.1109/tpwrs.2011.2142330Bishop, C. M. (1994). Neural networks and their applications. Review of Scientific Instruments, 65(6), 1803-1832. doi:10.1063/1.1144830Taylor, J. W., & Buizza, R. (2002). Neural network load forecasting with weather ensemble predictions. IEEE Transactions on Power Systems, 17(3), 626-632. doi:10.1109/tpwrs.2002.800906Chu, W.-C., Chen, Y.-P., Xu, Z.-W., & Lee, W.-J. (2011). Multiregion Short-Term Load Forecasting in Consideration of HI and Load/Weather Diversity. IEEE Transactions on Industry Applications, 47(1), 232-237. doi:10.1109/tia.2010.2090440Zhang, W. Y., Hong, W.-C., Dong, Y., Tsai, G., Sung, J.-T., & Fan, G. (2012). Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting. Energy, 45(1), 850-858. doi:10.1016/j.energy.2012.07.006Hong, W.-C. (2011). Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm. Energy, 36(9), 5568-5578. doi:10.1016/j.energy.2011.07.015Nose-Filho, K., Lotufo, A. D. P., & Minussi, C. R. (2011). Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network. IEEE Transactions on Power Delivery, 26(4), 2862-2869. doi:10.1109/tpwrd.2011.2166566Hernández, 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/en5125215Good, R. P., Kost, D., & Cherry, G. A. (2010). Introducing a Unified PCA Algorithm for Model Size Reduction. IEEE Transactions on Semiconductor Manufacturing, 23(2), 201-209. doi:10.1109/tsm.2010.2041263Hernández, L., Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., … Gómez, J. (2012). A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework. Sensors, 12(9), 11571-11591. doi:10.3390/s120911571Razavi, 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.2011.2163169http://www.ree.es/sistema_electrico/pdf/indel/Atlas_INDEL_REE.pdfHsu, C.-C., & Chen, C.-Y. (2003). Regional load forecasting in Taiwan––applications of artificial neural networks. Energy Conversion and Management, 44(12), 1941-1949. doi:10.1016/s0196-8904(02)00225-

Model Hibrida ARIMAX dan Deep Learning Neural Network untuk Peramalan Beban Listrik Jangka Pendek di PT. Indonesia Power UP Bali

Energi listrik tidak dapat langsung disimpan dalam skala besar dan hanya dapat digunakan saat dibutuhkan saja. Oleh karena itu energi listrik yang dibangkitkan di pembangkit harus sama dengan energi listrik yang digunakan oleh konsumen. Prediksi listrik yang tepat pada suatu daerah sangat diperlukan untuk mengoptimalkan persediaan kebutuhan listrik. Penelitian ini dilakukan bertujuan untuk menerapkan metode Hibrida ARIMAX dan Deep Learning Neural Network untuk peramalan beban listrik jangka pendek. Data yang digunakan pada penelitian ini adalah data beban listrik mulai Januari 2014 hingga Desember 2017 sebanyak 1461 observasi. Kajian yang digunakan dibagi menjadi dua kajian yaitu kajian simulasi dan kajian terapan. Hasil kajian simulasi menunjukkan bahwa metode Hibrida ARIMAX dan Deep Learning Neural Network menghasilkan hasil peramalan yang lebih baik untuk horizon medium dan long, sementara hasil lebih beragam diperoleh pada horizon short. Untuk kajian terapan, menunjukkan bahwa hasil peramalan menggunakan metode Deep Learning Neural Network menghasilkan hasil ramalan yang lebih baik untuk horizon medium dan long, sementara Hibrida ARIMAX dan Deep Learning Neural Network mendominasi pada horizon short. Pada kedua kajian model Hibrida ARIMAX-DLNN tidak selalu lebih unggul dibanding metode lainnya. Hal ini membuktikan bahwa metode yang lebih kompleks tidak selalu memberikan nilai akurasi ramalan yang lebih baik. Peramalan beban listrik dilakukan berdasarkan metode terbaik yang diperoleh pada horizon short dikarenakan pada umumnya semakin pendek periode peramalan maka akurasi yang dihasilkan semakin baik. =========================================================== Electrical energy can not be directly stored on a large scale and can only be used when needed only. Therefore, the electrical energy generated in the power plant must be equal to the electrical energy used by the consumer. Precise electrical prediction in a region is needed to optimize the supply of electricity needs. The aim of this research is to apply Hybrid ARIMAX and Deep Learning Neural Network method to forecast short-term electrical load. Data used in this research is electric load data from January 2014 to December 2017 as many as 1461 observations. The study used is divided into two studies namely simulation studies and applied studies. The results of the simulation study show that the Hybrid ARIMAX and Deep Learning Neural Network method produce better forecasting results for medium and long horizons, while more diverse results are obtained on the short horizon. For applied studies, indicating that forecasting results using the Deep Learning Neural Network method resulted in better outcomes for medium and long horizons, while Hybrid ARIMAX and Deep Learning Neural Network method dominated on the short horizon. In both studies the Hybrid ARIMAX-DLNN model is not always superior to other methods. This proves that more complex methods do not always provide better prediction accuracy values. Power load forecasting is based on the best method obtained on the short horizon because in general the shorter the forecast period the better the accuracy

Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks

Finding suitable forecasting methods for an effective management of energy resources is of paramount importance for improving the efficiency in energy consumption and decreasing its impact on the environment. Natural gas is one of the main sources of electrical energy in Algeria and worldwide. To address this demand, this paper introduces a novel hybrid forecasting approach that resolves the two-stage method's deficiency, by designing a Multi Layered Perceptron (MLP) neural network as a nonlinear forecasting monitor. This model estimates the next day gas consumption profile and selects one of several local models to perform the forecast. The study focuses firstly on an analysis and clustering of natural gas daily consumption profiles, and secondly on building a comprehensive Long Short Term Memory (LSTM) recurrent models according to load behavior. The results are compared with four benchmark approaches: the MLP neural network approach, LSTM, seasonal time series with exogenous variables models and multiple linear regression models. Compared with these alternative approaches and their high dependence on historical loads, the proposed approach presents a new efficient functionality. It estimates the next day consumption profile, which leads to a significant improvement of the forecasting accuracy, especially for days with exceptional customers consumption behavior change

Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

[EN] Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calendar effect. The multiple seasonal Holt-Winters models are widely used due to the great precision and simplicity that they offer. Usually, these models relate this calendar effect to external variables that contribute to modification of their forecasts a posteriori. In this work, a new point of view is presented, where the calendar effect constitutes a built-in part of the Holt-Winters model. In particular, the proposed model incorporates discrete-interval moving seasonalities. Moreover, a clear example of the application of this methodology to situations that are difficult to treat, such as the days of Easter, is presented. The results show that the proposed model performs well, outperforming the regular Holt-Winters model and other methods such as artificial neural networks and Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS) methods.The authors would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-8888209C2-1-R.Trull, Ó.; García-Díaz, JC.; Troncoso, A. (2019). Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies. 12(6):1-16. https://doi.org/10.3390/en12061083S116126Garrués-Irurzun, J., & López-García, S. (2009). Red Eléctrica de España S.A.: Instrument of regulation and liberalization of the Spanish electricity market (1944–2004). Renewable and Sustainable Energy Reviews, 13(8), 2061-2069. doi:10.1016/j.rser.2009.01.028Roldan-Fernandez, J., Gómez-Quiles, C., Merre, A., Burgos-Payán, M., & Riquelme-Santos, J. (2018). Cross-Border Energy Exchange and Renewable Premiums: The Case of the Iberian System. Energies, 11(12), 3277. doi:10.3390/en11123277Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. IEEE Transactions on Power Systems, 18(3), 1014-1020. doi:10.1109/tpwrs.2002.804943Juberias, G., Yunta, R., Garcia Moreno, J., & Mendivil, C. (1999). A new ARIMA model for hourly load forecasting. 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333). doi:10.1109/tdc.1999.755371Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421. doi:10.1016/j.energy.2009.06.034Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8), 799-805. doi:10.1057/palgrave.jors.2601589Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204(1), 139-152. doi:10.1016/j.ejor.2009.10.003Ko, C.-N., & Lee, C.-M. (2013). Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy, 49, 413-422. doi:10.1016/j.energy.2012.11.015Rana, M., & Koprinska, I. (2016). Forecasting electricity load with advanced wavelet neural networks. Neurocomputing, 182, 118-132. doi:10.1016/j.neucom.2015.12.004Baliyan, A., Gaurav, K., & Mishra, S. K. (2015). A Review of Short Term Load Forecasting using Artificial Neural Network Models. Procedia Computer Science, 48, 121-125. doi:10.1016/j.procs.2015.04.160Yang, Z., Ce, L., & Lian, L. (2017). Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Applied Energy, 190, 291-305. doi:10.1016/j.apenergy.2016.12.130Ghadimi, N., Akbarimajd, A., Shayeghi, H., & Abedinia, O. (2018). Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy, 161, 130-142. doi:10.1016/j.energy.2018.07.088Troncoso Lora, A., Riquelme Santos, J. M., Riquelme, J. C., Gómez Expósito, A., & Martínez Ramos, J. L. (2004). Time-Series Prediction: Application to the Short-Term Electric Energy Demand. Lecture Notes in Computer Science, 577-586. doi:10.1007/978-3-540-25945-9_57Martinez Alvarez, F., Troncoso, A., Riquelme, J. C., & Aguilar Ruiz, J. S. (2011). Energy Time Series Forecasting Based on Pattern Sequence Similarity. IEEE Transactions on Knowledge and Data Engineering, 23(8), 1230-1243. doi:10.1109/tkde.2010.227Cancelo, J. R., Espasa, A., & Grafe, R. (2008). Forecasting the electricity load from one day to one week ahead for the Spanish system operator. International Journal of Forecasting, 24(4), 588-602. doi:10.1016/j.ijforecast.2008.07.005TORRÓ, H., MENEU, V., & VALOR, E. (2003). Single Factor Stochastic Models with Seasonality Applied to Underlying Weather Derivatives Variables. The Journal of Risk Finance, 4(4), 6-17. doi:10.1108/eb022969Darbellay, G. A., & Slama, M. (2000). Forecasting the short-term demand for electricity. International Journal of Forecasting, 16(1), 71-83. doi:10.1016/s0169-2070(99)00045-xMoral-Carcedo, J., & Vicéns-Otero, J. (2005). Modelling the non-linear response of Spanish electricity demand to temperature variations. Energy Economics, 27(3), 477-494. doi:10.1016/j.eneco.2005.01.003Erişen, E., Iyigun, C., & Tanrısever, F. (2017). Short-term electricity load forecasting with special days: an analysis on parametric and non-parametric methods. Annals of Operations Research. doi:10.1007/s10479-017-2726-6Arora, S., & Taylor, J. W. (2013). Short-Term Forecasting of Anomalous Load Using Rule-Based Triple Seasonal Methods. IEEE Transactions on Power Systems, 28(3), 3235-3242. doi:10.1109/tpwrs.2013.2252929Arora, S., & Taylor, J. W. (2018). Rule-based autoregressive moving average models for forecasting load on special days: A case study for France. European Journal of Operational Research, 266(1), 259-268. doi:10.1016/j.ejor.2017.08.056Bermúdez, J. D. (2013). Exponential smoothing with covariates applied to electricity demand forecast. European J. of Industrial Engineering, 7(3), 333. doi:10.1504/ejie.2013.054134Göb, R., Lurz, K., & Pievatolo, A. (2013). Electrical load forecasting by exponential smoothing with covariates. Applied Stochastic Models in Business and Industry, 29(6), 629-645. doi:10.1002/asmb.2008Chatfield, C. (1978). The Holt-Winters Forecasting Procedure. Applied Statistics, 27(3), 264. doi:10.2307/234716

Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability

Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. Apart from introduction and references the paper is organized as follows. The section 2 presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting