Deep belief network based electricity load forecasting: An analysis of Macedonian case

A number of recent studies use deep belief networks (DBN) with a great success in various applications such as image classification and speech recognition. In this paper, a DBN made up from multiple layers of restricted Boltzmann machines is used for electricity load forecasting. The layer-by-layer unsupervised training procedure is followed by fine-tuning of the parameters by using a supervised back-propagation training method. Our DBN model was applied to short-term electricity load forecasting based on the Macedonian hourly electricity consumption data in the period 2008–2014. The obtained results are not only compared with the latest actual data, but furthermore, they are compared with the predicted data obtained from a typical feed-forward multi-layer perceptron neural network and with the forecasted data provided by the Macedonian system operator (MEPSO). The comparisons show that the applied model is not only suitable for hourly electricity load forecasting of the Macedonian electric power system, it actually provides superior results than the ones obtained using traditional methods. The mean absolute percentage error (MAPE) is reduced by up to 8.6% when using DBN, compared to the MEPSO data for the 24-h ahead forecasting, and the MAPE for daily peak forecasting is reduced by up to 21%.

[1]  Aleksandar Dedinec,et al.  Correlation of variables with electricity consumption data , 2016 .

[2]  Francesco Piazza,et al.  A review of datasets and load forecasting techniques for smart natural gas and water grids: Analysis and experiments , 2015, Neurocomputing.

[3]  Philippe Lauret,et al.  Bayesian neural network approach to short time load forecasting , 2008 .

[4]  Yu-Hsiang Hsiao,et al.  Household Electricity Demand Forecast Based on Context Information and User Daily Schedule Analysis From Meter Data , 2015, IEEE Transactions on Industrial Informatics.

[5]  Daryoush Habibi,et al.  Modelling self-optimised short term load forecasting for medium voltage loads using tunning fuzzy systems and Artificial Neural Networks , 2015 .

[6]  Wan He,et al.  Deep neural network based load forecast , 2014 .

[7]  Na Tang,et al.  Application of a Load Forecasting Model Based on Improved Grey Neural Network in the Smart Grid , 2011 .

[8]  Renato A. Krohling,et al.  Time Series Prediction Using Restricted Boltzmann Machines and Backpropagation , 2015, ITQM.

[9]  Jordan Pop-Jordanov,et al.  Low emissions development pathways of the Macedonian energy sector , 2016 .

[10]  M. M. Ardehali,et al.  LONG-TERM ELECTRICAL ENERGY CONSUMPTION FORECASTING FOR DEVELOPING AND DEVELOPED ECONOMIES BASED ON DIFFERENT OPTIMIZED MODELS AND HISTORICAL DATA TYPES , 2014 .

[11]  Christopher Nichols,et al.  The impacts of shale gas supply and climate policies on energy security: The U.S. energy system analysis based on MARKAL model , 2014 .

[12]  Grzegorz Dudek.  Pattern-based local linear regression models for short-term load forecasting , 2016 .

[13]  F. M. Andersen,et al.  Long term forecasting of hourly electricity consumption in local areas in Denmark , 2013 .

[14]  Aidong Zhang,et al.  Identifying informative risk factors and predicting bone disease progression via deep belief networks. , 2014, Methods.

[15]  Andrew D. Ball,et al.  An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks , 2014, Expert Syst. Appl..

[16]  Fredrik Wallin,et al.  Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting , 2012 .

[17]  Afshin Afshari,et al.  Short-term Forecasting of the Abu Dhabi Electricity Load Using Multiple Weather Variables , 2015 .

[18]  Aayushi Goel,et al.  Regression Based Forecast of Electricity Demand of New Delhi , 2014 .

[19]  Florentina Paraschiv,et al.  Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks , 2016 .

[20]  Yong Chen,et al.  Multi-pose face ensemble classification aided by Gabor features and deep belief nets , 2016 .

[21]  Ertuğrul Çam,et al.  Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines , 2015 .

[22]  Bernd Möller,et al.  Heat Roadmap Europe: Combining district heating with heat savings to decarbonise the EU energy system , 2014 .

[23]  V. Bianco,et al.  Analysis and forecasting of nonresidential electricity consumption in Romania , 2010 .

[24]  Kunikazu Kobayashi,et al.  Time series forecasting using a deep belief network with restricted Boltzmann machines , 2014, Neurocomputing.

[25]  Jaime Lloret,et al.  Artificial neural networks for short-term load forecasting in microgrids environment , 2014 .

[26]  Y. Bor,et al.  The long-term forecast of Taiwan’s energy supply and demand: LEAP model application , 2011 .

[27]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[28]  Xiao Liu,et al.  DeepChart: Combining deep convolutional networks and deep belief networks in chart classification , 2016, Signal Process..

[29]  Paula Varandas Ferreira,et al.  Renewable energy scenarios in the Portuguese electricity system , 2014 .

[30]  TranVan Tung,et al.  An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks , 2014 .

[31]  Zheng-Xin Wang,et al.  Optimal modeling and forecasting of the energy consumption and production in China , 2014 .

[32]  Coşkun Hamzaçebi,et al.  Forecasting the annual electricity consumption of Turkey using an optimized grey model , 2014 .

[33]  Ali Badri,et al.  Application of Artificial Neural Networks and Fuzzy logic Methods for Short Term Load Forecasting , 2012 .

[34]  James W. Taylor,et al.  An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting , 2010, Neural Networks.

[35]  Le Zhang,et al.  Ensemble deep learning for regression and time series forecasting , 2014, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL).

[36]  Ayman M. Eldeib,et al.  Breast cancer classification using deep belief networks , 2016, Expert Syst. Appl..

[37]  D. Dijk,et al.  Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices , 2013 .

[38]  S.A. Soman,et al.  An expert system approach to short-term load forecasting for Reliance Energy Limited, Mumbai , 2006, 2006 IEEE Power India Conference.

[39]  Zafar A. Khan,et al.  Load forecasting, dynamic pricing and DSM in smart grid: A review , 2016 .

[40]  Khuram Pervez Amber,et al.  Electricity consumption forecasting models for administration buildings of the UK higher education sector , 2015 .

[41]  Ali Deihimi,et al.  Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction , 2013 .

[42]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[43]  Danijela Nikolić,et al.  The impact of the mean daily air temperature change on electricity consumption , 2015 .

[44]  A Slobodan Ilic,et al.  HYBRID ARTIFICIAL NEURAL NETWORK SYSTEM FOR SHORT-TERM LOAD FORECASTING , 2012 .

[45]  V. Bianco,et al.  Electricity consumption forecasting in Italy using linear regression models , 2009 .

[46]  Shen Furao,et al.  Forecasting exchange rate using deep belief networks and conjugate gradient method , 2015, Neurocomputing.

[47]  Alagan Anpalagan,et al.  Improved short-term load forecasting using bagged neural networks , 2015 .

[48]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[49]  Abbas Khosravi,et al.  Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[50]  S. Huang,et al.  Short-term load forecasting using threshold autoregressive models , 1997 .

[51]  Wen-Huang Cheng,et al.  A comparative study of data fusion for RGB-D based visual recognition , 2016, Pattern Recognit. Lett..