A fuzzy logic controller based mid-term load forecasting with renewable penetration in Assam, India

Mayur Barman, N. B. Dev Choudhury, Sadasiva Behera

Abstract


An accurate mid-term load forecasting (MTLF) tool is an essential part of power systems planning and sustainable development. In order to compensate the extra uncertainties, the power systems with high renewable penetration need even more accurate MTLF tool. The electric load demand is highly prejudiced by the thermal inertia due to the local climatic factors. Therefore, the accuracy of an MTLF method is highly dependent on the incorporated climatic factors. This paper proposes a fuzzy logic comptroller based MTLF method with renewable penetration. In order to achieve a higher degree of forecasting accuracy proposed method incorporated several climatic factors in the forecasting process. The study is done in Assam, a state of India and the proposed method is utilized to forecast the daily average load demand for one month. The forecasting accuracy of the proposed method is compared with one of most commonly used tool for MTLF called artificial neural network (ANN). The empirical results affirm the superiority of the proposed method over the ANN.

Full Text:

PDF

References


C. Cecati, J. Kolbusz, P. Ró˙zycki, P. Siano, and B. M. Wilamowski, “A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies,†IEEE Trans. Ind. Electron., vol. 62, no. 10, pp. 6519–6529, 2015.

R. V. Kale and S. D. Pohekar, “Electricity demand and supply scenarios for Maharashtra (India) for 2030: An application of long range energy alternatives planning,†Energy Policy, vol. 72, pp. 1–13, 2014.

R. Mamlook, O. Badran, and E. Abdulhadi, “A fuzzy inference model for short-term load forecasting,†Energy Policy, vol. 37, no. 4, pp. 1239–1248, 2009.

Ö. F. Ertugrul, “Forecasting electricity load by a novel recurrent extreme learning machines approach,†Int. J. Electr. Power Energy Syst., vol. 78, pp. 429–435, 2016.

N. Amjady, “Short-term hourly load forecasting using time-series modeling with peak load estimation capability,†IEEE Trans. Power Syst., vol. 16, no. 4, pp. 798–805, 2001.

S. Mirasgedis et al., “Models for mid-term electricity demand forecasting incorporating weather influences,†Energy, vol. 31, no. 2–3, pp. 208–227, 2006.

D. Ali, M. Yohanna, M. I. Puwu, and B. M. Garkida, “Long-term load forecast modelling using a fuzzy logic approach,†Pacific Sci. Rev. A Nat. Sci. Eng., vol. 18, no. November, pp. 1–5, 2016.

S. J. Huang and K. R. Shih, “Short-term load forecasting via ARMA model identification including non-Gaussian process considerations,†IEEE Trans. Power Syst., vol. 18, no. 2, pp. 673–679, 2003.

S. S. Pappas, L. Ekonomou, D. C.Karamousantas, G. E. Chatzarakis, S. K. Katsikas, and P. Liatsis, “Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models,†Energy, vol. 33, no. 9, pp. 1353–1360, 2008.

M. De Felice, A. Alessandri, and F. Catalano, “Seasonal climate forecasts for medium-term electricity demand forecasting,†Appl. Energy, vol. 137, pp. 435–444, 2015.

P. Zhou, B. W. Ang, and K. L. Poh, “A trigonometric grey prediction approach to forecasting electricity demand,†Energy, vol. 31, no. 14, pp. 2503–2511, 2006.

H. M. Al-Hamadi and S. A. Soliman, “Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model,†Electr. Power Syst. Res., vol. 68, no. 1, pp. 47–59, 2004.

C.-N. Ko and C.-M. Lee, “Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter,†Energy, vol. 49, pp. 413–422, 2013.

M. Mordjaoui, S. Haddad, A. Medoued, and A. Laouafi, “Electric load forecasting by using dynamic neural network,†Int. J. Hydrogen Energy, pp. 1–9, 2017.

M. Barman and N. B. D. Choudhury, “Artificial Neural Network Based Electricity Price Forecasting Using Levenberg-Marquardt Algorithm,†Int. J. Control Theory Appl., vol. 10, no. 19, pp. 127–136, 2017.

M. S. S. Rao, S. A. Soman, B. L. Menezes, P. Chawande, P. Dipti, and T. Ghanshyam, “An expert system approach to short-term load forecasting for Reliance Energy Limited, Mumbai,†2006 IEEE Power India Conf., vol. 2005, pp. 763–768, 2005.

A. Azadeh, M. Saberi, and O. Seraj, “An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran,†Energy, vol. 35, no. 6, pp. 2351–2366, 2010.

B. Yildiz, J. I. Bilbao, and A. B. Sproul, “A review and analysis of regression and machine learning models on commercial building electricity load forecasting,†Renew. Sustain. Energy Rev., vol. 73, no. December 2016, pp. 1104–1122, 2017.

S. Rahman, “Formulation and Analysis of a Rule-Based Short-Term Load Forecasting Algorithm,†Proc. IEEE, vol. 78, no. 5, pp. 805–816, 1990.

D. C. Park, M. a. El-Sharkawi, R. J. Marks, L. E. Atlas, and M. J. Damborg, “Electric load forecasting using an artificial neural network,†IEEE Trans. Power Syst., vol. 6, no. 2, pp. 442–449, 1991.

P. Mandal, T. Senjyu, N. Urasaki, and T. Funabashi, “A neural network based several-hour-ahead electric load forecasting using similar days approach,†Int. J. Electr. Power Energy Syst., vol. 28, no. 6, pp. 367–373, 2006.

T. Senjyu, H. Takara, K. Uezato, and T. Funabashi, “One-hour-ahead load forecasting using neural network,†Power Syst. IEEE …, vol. 17, no. 1, pp. 113–118, 2002.

I. Drezga, “Short-term load forecasting with local ann predictors,†IEEE Trans. Power Syst., vol. 14, no. 3, pp. 844–850, 1999.

A. H. Neto and F. A. S. Fiorelli, “Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption,†Energy Build., vol. 40, no. 12, pp. 2169–2176, 2008.

A. Jain, E. Srinivas, and R. Rauta, “Short term load forecasting using fuzzy adaptive inference and similarity,†2009 World Congr. Nat. Biol. Inspired Comput., pp. 1743–1748, 2009.

M. Barman, S. Mahapatra, D. Palit, and M. K. Chaudhury, “Performance and impact evaluation of solar home lighting systems on the rural livelihood in Assam, India,†Energy Sustain. Dev., vol. 38, pp. 10–20, 2017.

S. Sepasi, E. Reihani, A. M. Howlader, L. R. Roose, and M. M. Matsuura, “Very short term load forecasting of a distribution system with high PV penetration,†Renew. Energy, vol. 106, pp. 142–148, 2017.

A. Hussain, M. Rahman, and J. A. Memon, “Forecasting electricity consumption in Pakistan: The way forward,†Energy Policy, vol. 90, pp. 73–80, 2016.

E. Ceperic, V. Ceperic, and a. Baric, “A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines,†IEEE Trans. Power Syst., vol. Early Acce, no. 4, pp. 4356–4364, 2013.

G. Dudek, “Pattern similarity-based methods for short-term load forecasting - Part 1: Principles,†Appl. Soft Comput. J., vol. 37, pp. 277–287, 2015.

CEA (Central Electrical Authority). http://www.cea.nic.in/,2017;[Accessed on 17th September, 2017].

MNRE (Ministry of New and Renewable Energy). http://www.mnre.gov.in/, 2017; [Accessed on 19th September, 2017].


Refbacks

  • There are currently no refbacks.


------------------------------------------------------------------------------------------------------------------------

The ADBU Journal of Engineering Technology (AJET)" ISSN:2348-7305

This journal is published under the terms of the Creative Commons Attribution (CC-BY) (http://creativecommons.org/licenses/)

Number of Visitors to this Journal: