Short-term Load Forecasting based on ABC and ANN for Smart Grids

Yıl 2016, , 38 - 43, 26.12.2016

Hasan Huseyin Cevik , Hüseyin Harmancı Mehmet Çunkaş

https://doi.org/10.18201/ijisae.266014

Cited By: 4

Öz

Short term load forecasting is a subject about estimating future
electricity consumption for a time interval from one hour to one week and it
has a vital importance for the operation of a power system and smart grids.
This process is mandatory for distribution companies and big electricity
consumers, especially in liberalized energy markets. Electricity generation
plans are made according to the amount of electricity consumption forecasts. If
the forecast is overestimated, it leads to the start-up of too many units supplying
an unnecessary level of reserve, therefore the production cost is increased. On
the contrary if the forecast is underestimated, it may result in a risky
operation and consequently power outages can occur at the power system. In this
study, a hybrid method based on the combination of Artificial Bee Colony (ABC)
and Artificial Neural Network (ANN) is developed for short term load
forecasting. ABC algorithm is used in ANN learning process and it optimizes the
neuron connections weights of ANN. Historical load, temperature difference and
season are selected as model inputs. While three years hourly data is selected
as training data, one year hourly data is selected as testing data. The results
show that the application of this hybrid system produce forecast values close
to the actual values.

Anahtar Kelimeler

Artificial bee colony, Artificial neural network, Hybrid method, Short term load forecasting

Kaynakça

• [1] Kelo, S. Kelo, S. and S.V. Dudul, Short-term Maharashtra state electrical power load prediction with special emphasis on seasonal changes using a novel focused time lagged recurrent neural network based on time delay neural network model. Expert Systems with Applications, 2011. 38(3): p. 1554-1564.
• [2] Clastres, C., Smart grids: Another step towards competition, energy security and climate change objectives. Energy Policy, 2011. 39(9): p. 5399-5408.
• [3] Singh, A.K., Smart Grid Load Forecasting. International Journal of Engineering Research and Applications (IJERA), 2012.
• [4] Feng, Y. and S.M. Ryan, Day-ahead hourly electricity load modeling by functional regression. Applied Energy, 2016. 170: p. 455-465.
• [5] Panapakidis, I.P., Clustering based day-ahead and hour-ahead bus load forecasting models. International Journal of Electrical Power & Energy Systems, 2016. 80: p. 171-178.
• [6] Dudek, G., Pattern-based local linear regression models for short-term load forecasting. Electric Power Systems Research, 2016. 130: p. 139-147.
• [7] Friedrich, L. and A. Afshari, Short-term forecasting of the Abu Dhabi electricity load using multiple weather variables. Energy Procedia, 2015. 75: p. 3014-3026.
• [8] Sun, X., et al., An Efficient Approach to Short-Term Load Forecasting at the Distribution Level. IEEE Transactions on Power Systems, 2016. 31(4): p. 2526-2537.
• [9] H. H. Cevik and M. Cunkas, "A comparative study of artificial neural network and ANFIS for short term load forecasting," in Proc. 6th International Conference on Electronics, Computers and Artificial Intelligence, 2014, vol. 5, pp. 29-34
• [10] H. H. Çevik, "Short term electrical load forecasting of Turkey," M.S. thesis, Selçuk University Directorate of Graduate School Natural and Applied of Science, Konya-Turkey, 2013.
• [11] H. H. Çevik and M. Çunkaş, "Short-term load forecasting using fuzzy logic and ANFIS," Neural Computing and Applications, vol. 26, pp. 1-13, 2015.
• [12] Abdoos, A., M. Hemmati, and A.A. Abdoos, Short term load forecasting using a hybrid intelligent method. Knowledge-Based Systems, 2015. 76: p. 139-147.
• [13] Liu, M. Short term load forecasting based on the particle swarm optimization with simulated annealing. in Control Conference (CCC), 2011 30th Chinese. 2011. IEEE.
• [14] Høverstad, B.A., et al., Short-term load forecasting with seasonal decomposition using evolution for parameter tuning. IEEE Transactions on Smart Grid, 2015. 6(4): p. 1904-1913.
• [15] Castelli, M., L. Vanneschi, and M. De Felice, Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case. Energy Economics, 2015. 47: p. 37-41.
• [16] Li, S., L. Goel, and P. Wang, An ensemble approach for short-term load forecasting by extreme learning machine. Applied Energy, 2016. 170: p. 22-29.
• [17] Guo, Z., et al., Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renewable Energy, 2012. 37(1): p. 241-249.
• [18] Chen, D.S. and R.C. Jain, A robust backpropagation learning algorithm for function approximation. IEEE Transactions on Neural Networks, 1994. 5(3): p. 467-479.
• [19] Karaboga, D. and B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 2007. 39(3): p. 459-471.
• [20] Karaboga, D., B. Akay, and C. Ozturk. i Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks in International Conference on Modeling Decisions for Artificial Intelligence. 2007. Spring.