Deep Learning for Renewable Power Forecasting: An Approach Using LSTM Neural Networks

Load forecasting has become crucial in recent years
and become popular in forecasting area. Many different power
forecasting models have been tried out for this purpose. Electricity
load forecasting is necessary for energy policies, healthy and reliable
grid systems. Effective power forecasting of renewable energy load
leads the decision makers to minimize the costs of electric utilities
and power plants. Forecasting tools are required that can be used
to predict how much renewable energy can be utilized. The purpose
of this study is to explore the effectiveness of LSTM-based neural
networks for estimating renewable energy loads. In this study, we
present models for predicting renewable energy loads based on
deep neural networks, especially the Long Term Memory (LSTM)
algorithms. Deep learning allows multiple layers of models to learn
representation of data. LSTM algorithms are able to store information
for long periods of time. Deep learning models have recently been
used to forecast the renewable energy sources such as predicting
wind and solar energy power. Historical load and weather information
represent the most important variables for the inputs within the
power forecasting models. The dataset contained power consumption
measurements are gathered between January 2016 and December
2017 with one-hour resolution. Models use publicly available data
from the Turkish Renewable Energy Resources Support Mechanism.
Forecasting studies have been carried out with these data via deep
neural networks approach including LSTM technique for Turkish
electricity markets. 432 different models are created by changing
layers cell count and dropout. The adaptive moment estimation
(ADAM) algorithm is used for training as a gradient-based optimizer
instead of SGD (stochastic gradient). ADAM performed better than
SGD in terms of faster convergence and lower error rates. Models
performance is compared according to MAE (Mean Absolute Error)
and MSE (Mean Squared Error). Best five MAE results out of
432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting
performance of the proposed LSTM models gives successful results
compared to literature searches.




References:
[1] Hisashi Takeda, Yoshiyasu Tamura, and Seisho Sato. Using the ensemble
kalman filter for electricity load forecasting and analysis. Energy,
104:184 – 198, 2016.
[2] H. M. Al-Hamadi and S. A. Soliman. Fuzzy short-term electric
load forecasting using kalman filter. IEE Proceedings - Generation,
Transmission and Distribution, 153(2):217–227, March 2006.
[3] S.Sp. Pappas, L. Ekonomou, D.Ch. Karamousantas, G.E. Chatzarakis,
S.K. Katsikas, and P. Liatsis. Electricity demand loads modeling using
autoregressive moving average arma models. Energy, 33(9):1353 – 1360,
2008.
[4] Cheng-Ming Lee and Chia-Nan Ko. Short-term load forecasting using
lifting scheme and arima models. Expert Systems with Applications,
38(5):5902 – 5911, 2011.
[5] Grzegorz Dudek. Pattern-based local linear regression models for
short-term load forecasting. Electric Power Systems Research, 130:139
– 147, 2016.
[6] Aldo Goia, Caterina May, and Gianluca Fusai. Functional clustering
and linear regression for peak load forecasting. International Journal of
Forecasting, 26(4):700 – 711, 2010.
[7] G. Pan and Q. Dou. Load forecasting model based on multi-agents
cooperation. In 2012 8th International Conference on Natural
Computation, pages 1197–1202, May 2012.
[8] Lopez-Rodriguez and M. Hernandez-Tejera. Infrastructure based on
supernodes and software agents for the implementation of energy
markets in demand-response programs. Applied Energy, 158:1–11, 2015.
[9] JinXing Che and JianZhou Wang. Short-term load forecasting using
a kernel-based support vector regression combination model. Applied
Energy, 132:602 – 609, 2014.
[10] Wei-Chiang Hong. Electric load forecasting by support vector model.
Applied Mathematical Modelling, 33(5):2444 – 2454, 2009.
[11] Wan He. Load forecasting via deep neural networks. Procedia
Computer Science, 122:308 – 314, 2017. 5th International Conference
on Information Technology and Quantitative Management, ITQM 2017.
[12] Seunghyoung Ryu, Jaekoo Noh, and Hongseok Kim. Deep neural
network based demand side short term load forecasting. In
2016 IEEE International Conference on Smart Grid Communications
(SmartGridComm), pages 308–313, Nov 2016.[13] S. Hosein and P. Hosein. Load forecasting using deep neural networks.
In 2017 IEEE Power Energy Society Innovative Smart Grid Technologies
Conference (ISGT), pages 1–5, April 2017.
[14] Murat Kankal, Adem Akpinar, Murat ˙Ihsan Komurcu, and Talat Sukru
Ozsahin. Modeling and forecasting of turkey’s energy consumption
using socio-economic and demographic variables. Applied Energy,
88(5):1927 – 1939, 2011.
[15] Serhat Kucukali and Kemal Baris. Turkey’s short-term gross annual
electricity demand forecast by fuzzy logic approach. Energy Policy,
38(5):2438 – 2445, 2010. Greater China Energy: Special Section with
regular papers.
[16] Vassilis S. Kodogiannis, Mahdi Amina, and Ilias Petrounias. A
clustering-based fuzzy wavelet neural network modek for short-term
load forecasting. International Journal of Neural Systems,
23(5):1350024–1 – 1350024–19, 2013.
[17] Hesham K Alfares and Mohammad Nazeeruddin. Electric load
forecasting: literature survey and classification of methods. International
Journal of Systems Science, 33(1):23–34, 2002.
[18] Chin Wang Lou and Ming Chui Dong. A novel random fuzzy
neural networks for tackling uncertainties of electric load forecasting.
International Journal of Electrical Power & Energy Systems, 73:34–44,
2015.
[19] Fazil Gokgoz Fahrettin Filiz. Electricity price forecasting in turkey with
artificial neural network models. Investment Management and Financial
Innovations, 2016.
[20] Yann LeCun, Y Bengio, and Geoffrey Hinton. Deep learning.
521:436–44, 05 2015.
[21] Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink,
and Jürgen Schmidhuber. LSTM: A search space odyssey. CoRR,
abs/1503.04069, 2015.
[22] Heiko Hahn, Silja Meyer-Nieberg, and Stefan Pickl. Electric load
forecasting methods: Tools for decision making. European Journal of
Operational Research, 199(3):902 – 907, 2009.
[23] H. S. Hippert, C. E. Pedreira, and R. C. Souza. Neural networks for
short-term load forecasting: a review and evaluation. IEEE Transactions
on Power Systems, 16(1):44–55, Feb 2001.