Performance of a Hybrid Neural-Based Framework for Alternative Electricity Price Forecasting in the Smart Grid
DOI:
https://doi.org/10.13052/dgaej2156-3306.3731Keywords:
Electricity price forecasting, artificial neural networks, smart grids, electricity markets.Abstract
Electricity forecasting is an essential task for energy management systems
of microgrids deployed in smart grids. Accurate price forecasting will eventually
enhance the economic operation of microgrids. In this regard, the
literature is rich with studies focused on predicting electricity price data
using artificial neural networks. However, most of them consider a single
model such as multi-layer perceptron (MLP) and radial basis function (RBF)
to perform electricity price forecasting. In this paper, a hybrid framework
based on simultaneously utilizing MLP-RBF neural networks is presented to
predict the Iranian electricity market price. In addition, few works in literature
considered Iran’s electricity market as their case of analysis and investigation.
Forecasting results indicate that MLP neural networks outperform the
RBF neural networks. The values for the coefficient of determination (R)
corresponding to MLP and RBF neural networks are obtained 0.55 and 0.44,
respectively. However, the proposed hybrid framework performed better than
both MLP and RBF models with R-value equal to 0.71. In addition to this,
the MSE and RMSE values show the superiority of the proposed method to
the single methods.
Downloads
References
M. Halužan, M. Verbic, and J. Zori ˇ c, “Performance of alternative ´
electricity price forecasting methods: Findings from the Greek and
Hungarian power exchanges,” Applied Energy, vol. 277, p. 115599,
/11/01/ 2020.
C. García-Martos, J. Rodríguez, and M. J. Sánchez, “Forecasting electricity prices by extracting dynamic common factors: application to the
Iberian Market,” IET Generation, Transmission & Distribution, vol. 6,
no. 1, pp. 11–20 [Online]. Available: https://digital-library.theiet.org/c
ontent/journals/10.1049/iet-gtd.2011.0009
H. Khaloie, A. Anvari-Moghaddam, J. Contreras, and P. Siano, “Riskinvolved optimal operating strategy of a hybrid power generation company: A mixed interval-CVaR model,” Energy, vol. 232, p. 120975,
/10/01/ 2021.
R. Weron and A. Misiorek, “Forecasting spot electricity prices with time
series models,” Proceedings of the European Electricity Market EEM-05
Conference, 05/06 2005.
H. Khaloie, M. Mollahassani-Pour, and A. Anvari-Moghaddam, “Optimal Behavior of a Hybrid Power Producer in Day-Ahead and Intraday
Markets: A Bi-Objective CVaR-Based Approach,” IEEE Transactions
on Sustainable Energy, vol. 12, no. 2, pp. 931–943, 2021.
H. Zareipour, C. A. Canizares, and K. Bhattacharya, “Economic Impact
of Electricity Market Price Forecasting Errors: A Demand-Side Analysis,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 254–262,
E. Crisostomi, C. Gallicchio, A. Micheli, M. Raugi, and M. Tucci,
“Prediction of the Italian electricity price for smart grid applications,”
Neurocomputing, vol. 170, pp. 286–295, 2015/12/25/ 2015.
T. Qiu and J. Faraji, “Techno-economic optimization of a grid-connected
hybrid energy system considering electric and thermal load prediction,”
Energy Science & Engineering, https://doi.org/10.1002/ese3.906
vol. n/a, no. n/a, 2021/05/12 2021.
J. Faraji, H. Hashemi-Dezaki, and A. Ketabi, “Multi-year load growthbased optimal planning of grid-connected microgrid considering longterm load demand forecasting: A case study of Tehran, Iran,” Sustainable Energy Technologies and Assessments, vol. 42, p. 100827,
/12/01/ 2020.
J. Faraji, A. Ketabi, and H. Hashemi-Dezaki, “Developing an Energy
Management System for Optimal Operation of Prosumers Based on
a Modified Data-Driven Weather Forecasting Method,” in 2020 10th
Smart Grid Conference (SGC), 2020, pp. 1–6.
S. H. Latha and S. C. Mohan, “Centralized power control strategy for
kw nano grid for rustic electrification,” in 2012 International Conference on Emerging Trends in Science, Engineering and Technology
(INCOSET), 2012, pp. 456–461.
Y. T. Quek, W. L. Woo, and T. Logenthiran, “Smart Sensing of Loads
in an Extra Low Voltage DC Pico-Grid Using Machine Learning
Techniques,” IEEE Sensors Journal, vol. 17, no. 23, pp. 7775–7783,
P. Lombardi, M. Powalko, and K. Rudion, “Optimal operation of a
virtual power plant,” in 2009 IEEE Power & Energy Society General
Meeting, 2009, pp. 1–6.
J. Faraji, H. Hashemi-Dezaki, and A. Ketabi, “Stochastic operation
and scheduling of energy hub considering renewable energy sources’
uncertainty and N − 1 contingency,” Sustainable Cities and Society,
vol. 65, p. 102578, 2021/02/01/ 2021.
J. Faraji, A. Ketabi, H. Hashemi-Dezaki, M. Shafie-Khah, and J. P. S.
Catalão, “Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and
Load Forecasting,” IEEE Access, vol. 8, pp. 157284–157305, 2020.
A. Hasankhani and S. M. Hakimi, “Stochastic energy management
of smart microgrid with intermittent renewable energy resources in
electricity market,” Energy, vol. 219, p. 119668, 2021/03/15/ 2021.
P. Emrani-Rahaghi, H. Hashemi-Dezaki, and A. Hasankhani, “Optimal
stochastic operation of residential energy hubs based on plug-in hybrid
electric vehicle uncertainties using two-point estimation method,” Sustainable Cities and Society, vol. 72, p. 103059, 2021/09/01/ 2021.
S. M. Hakimi, A. Hasankhani, M. Shafie-khah, and J. P. S. Catalão,
“Stochastic planning of a multi-microgrid considering integration of
renewable energy resources and real-time electricity market,” Applied
Energy, vol. 298, p. 117215, 2021/09/15/ 2021.
I. Shah and F. Lisi, “Forecasting of electricity price through a functional
prediction of sale and purchase curves,” Journal of Forecasting, https:
//doi.org/10.1002/for.2624, vol. 39, no. 2, pp. 242–259, 2020/03/01
M. Cerjan, I. Krželj, M. Vidak, and M. Delimar, “A literature review with
statistical analysis of electricity price forecasting methods,” in Eurocon
, 2013, pp. 756–763.
C. Zhang, R. Li, H. Shi, and F. Li, “Deep learning for day-ahead
electricity price forecasting,” IET Smart Grid, https://doi.org/10.104
/iet-stg.2019.0258, vol. 3, no. 4, pp. 462–469, 2020/08/01 2020.
S. C. Chan, K. M. Tsui, H. C. Wu, Y. Hou, Y. Wu, and F. F. Wu,
“Load/Price Forecasting and Managing Demand Response for Smart
Grids: Methodologies and Challenges,” IEEE Signal Processing Magazine, vol. 29, no. 5, pp. 68–85, 2012.
J. Lago, F. De Ridder, and B. De Schutter, “Forecasting spot electricity prices: Deep learning approaches and empirical comparison
of traditional algorithms,” Applied Energy, vol. 221, pp. 386–405, 2018/07/01/ 2018.
J. Che and J. Wang, “Short-term electricity prices forecasting based on
support vector regression and Auto-regressive integrated moving average modeling,” Energy Conversion and Management, vol. 51, no. 10,
pp. 1911–1917, 2010/10/01/ 2010.
J. P. S. Catalao, H. M. I. Pousinho, and V. M. F. Mendes, “Neural
Networks and Wavelet Transform for Short-Term Electricity Prices
Forecasting,” in 2009 15th International Conference on Intelligent
System Applications to Power Systems, 2009, pp. 1–5.
I. P. Panapakidis and A. S. Dagoumas, “Day-ahead electricity price forecasting via the application of artificial neural network based models,”
Applied Energy, vol. 172, pp. 132–151, 2016/06/15/ 2016.
B. Neupane, K. S. Perera, Z. Aung, and W. L. Woon, “Artificial Neural
Network-based electricity price forecasting for smart grid deployment,”
in 2012 International Conference on Computer Systems and Industrial
Informatics, 2012, pp. 1–6.
S. Voronin and J. Partanen, “Price Forecasting in the Day-Ahead Energy
Market by an Iterative Method with Separate Normal Price and Price
Spike Frameworks,” Energies, vol. 6, no. 11, pp. 5897–5920, 2013.
G. Díaz, J. Coto, and J. Gómez-Aleixandre, “Prediction and explanation
of the formation of the Spanish day-ahead electricity price through
machine learning regression,” Applied Energy, vol. 239, pp. 610–625,
/04/01/ 2019.
M. N. Fekri, H. Patel, K. Grolinger, and V. Sharma, “Deep learning
for load forecasting with smart meter data: Online Adaptive Recurrent
Neural Network,” Applied Energy, vol. 282, p. 116177, 2021/01/15/
L. Li, C. J. Meinrenken, V. Modi, and P. J. Culligan, “Short-term
apartment-level load forecasting using a modified neural network with
selected auto-regressive features,” Applied Energy, vol. 287, p. 116509,
/04/01/ 2021.
X.-J. Dong, J.-N. Shen, G.-X. He, Z.-F. Ma, and Y.-J. He, “A general radial basis function neural network assisted hybrid modeling
method for photovoltaic cell operating temperature prediction,” Energy,
vol. 234, p. 121212, 2021/11/01/ 2021.
S. Jafarzadeh Ghoushchi, S. Manjili, A. Mardani, and M. K. Saraji, “An
extended new approach for forecasting short-term wind power using
modified fuzzy wavelet neural network: A case study in wind power
plant,” Energy, vol. 223, p. 120052, 2021/05/15/ 2021.
J. Faraji, A. Abazari, M. Babaei, S. M. Muyeen, and M. Benbouzid,
“Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources,” Applied
Sciences, vol. 10, no. 8, 2020.
G. R. Yousefi, S. M. Kaviri, M. A. Latify, and I. Rahmati, “Electricity
industry restructuring in Iran,” Energy Policy, vol. 108, pp. 212–226,
/09/01/ 2017.
(2021). Iran Electricity Market [Online]. Available: http://www.irema.
ir/trading/buyers.
H. Zhang et al., “A combined model based on SSA, neural networks,
and LSSVM for short-term electric load and price forecasting,” Neural
Computing and Applications, vol. 33, no. 2, pp. 773–788, 2021/01/01
C.-J. Huang, Y. Shen, Y.-H. Chen, and H.-C. Chen, “A novel hybrid
deep neural network model for short-term electricity price forecasting,”
International Journal of Energy Research, https://doi.org/10.1002/er.5
, vol. 45, no. 2, pp. 2511–2532, 2021/02/01 2021.
G. Memarzadeh and F. Keynia, “Short-term electricity load and price
forecasting by a new optimal LSTM-NN based prediction algorithm,”
Electric Power Systems Research, vol. 192, p. 106995, 2021/03/01/
J. Abdi, B. Moshiri, and A. K. Sedigh, “Comparison of RBF and MLP
neural networks in short-term traffic flow forecasting,” in 2010 International Conference on Power, Control and Embedded Systems, 2010,
pp. 1–4.
J. Faraji, A. Ketabi, H. Hashemi-Dezaki, M. Shafie-Khah, and J. P. S.
Catalão, “Optimal Day-Ahead Scheduling and Operation of the Prosumer by Considering Corrective Actions Based on Very Short-Term
Load Forecasting,” IEEE Access, vol. 8, pp. 83561–83582, 2020.
R. A. Chinnathambi, S. J. Plathottam, T. Hossen, A. S. Nair, and P. Ranganathan, “Deep Neural Networks (DNN) for Day-Ahead Electricity
Price Markets,” in 2018 IEEE Electrical Power and Energy Conference
(EPEC), 2018, pp. 1–6.
S. R. Salkuti, “Short-term electrical load forecasting using radial basis
function neural networks considering weather factors,” Electrical Engineering, vol. 100, no. 3, pp. 1985–1995, 2018/09/01 2018.
