Abstract
For planning and operation of an energy management system, load forecasting (LF) is essential. For smooth power system operation (PS), LF enhances the energy-efficient and reliable operation. LF also helps to calculate energy supplied by utilities to meet the load plus the energy lost in the PS. Every day, it is necessary to schedule the power generation for the next day. So, short-term load forecasting (STLF) is used to calculate the power dispatch for the next day. In unit commitment, economic allocation of generation and maintenance schedules, STLF is also used. So, to make the STLF more effective, fuzzy logic (FL) is used here. FL is essential for weather-sensitive and historical load data for forecasting the load. The fuzzy decision rule identifies the nonlinear relationship between the input and output data. The historical load and hourly data like temperature, humidity (relative humidity) and wind speed are used for input data. For the training and testing, the hourly based load data are collected from the state load dispatch and communication center of Rajasthan Vidyut Prasaran Nigam, Jaipur (JVN). The triangular membership function of the fuzzy logic model is used to predict the load. The performance of the work is determined by the mean absolute percentage error (MAPE) and the MAPE value for pre-holiday (Saturday), holiday (Sunday), post-holiday, and working day is 0.37%, 0.24%, 0.09%, and 0.09%, respectively.
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References
D.K. Ranaweera, N.F. Hubele, G.G. Karady, Fuzzy logic for short term load forecasting. IEEE Electr. Power Energy Syst. 18(4), 215–222 (1996)
P. Ray, S.R. Arya, S. Nandkeolyar, Electric load forecasted by metaheuristic based back propagation approach. J. Green Eng. 7, 61–82 (2017)
S.K. Panda, P. Ray, D.P. Mishra, Short Term Load Forecasting using Metaheuristic Techniques, in IOP conference series: material science engineering, vol. 1033, p. 012016 (2021)
S.K. Panda, P. Ray, D.P. Mishra, An efficient short-term electric power load forecasting using hybrid techniques. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 12, 387–397 (2020)
P. Ray, S.K. Panda, D.P. Mishra, Short-term load forecasting using genetic Algorithm, in Springer international conference on computational intelligence in data mining (ICCIDM). vol. 711, pp. 863–872 (2019)
S. K. Panda, P Ray, D. P Mishra. Effectiveness of PSO on Short Term Load Forecasting, in Springer International Conference on Applications of Robotics in Industry Using Advanced Mechanisms (ARIAM). Learning and Analytics in Intelligent Systems, vol. 5, pp. 122–129 (2020)
S.K. Panda, P Ray, D.P. Mishra, Effectiveness of GA on short term load forecasting, in IEEE international conference on information technology (ICIT), pp. 27–32 (2019)
S.K. Panda, P. Ray, D.P. Mishra, Short term load forecasting using empirical mode decomposition (EMD), particle swarm optimization (PSO) and adaptive network-based fuzzy interference systems (ANFIS), in Springer International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA), vol. 1180, pp. 161–168 (2021)
S.K. Panda, P. Ray, D.P. Mishra, A study of machine learning techniques in short term load forecasting using ANN, in Springer international conference on intelligent and cloud computing. smart innovation, systems and technologies (ICICC), vol. 194, pp. 49–57 (2021)
R. Behera, B.B. Pati, B.P. Panigrahi, A long term load forecasting of an Indian grid for power system planning. J. Inst. Eng. India Ser. B 95, 279–285 (2014)
R.D. Rathor, A. Bharagava, Day ahead regional electrical load forecasting using ANFIS techniques. J. Inst. Eng. India Ser. B 101, 475–495 (2020)
C.K. Shiva, S.S. Gudadappanavar, B. Vedik, R. Babu, S. Raj, B. Bhattacharya, Fuzzy-based shunt VAR source placement and sizing by oppositional crow search algorithm. J. Control Autom. Electr. Syst. (2022). https://doi.org/10.1007/s40313-022-00903-4
M.S. Shaikh, C. Hua, S. Raj, S. Kumar, M. Hassan, M.M. Ansari, M.A. Jatoi, Optimal parameter estimation of 1-phase and 3-phase transmission line for various bundle conductors using modified whale optimization algorithm. Int. J. Electr. Power Energy Syst. (2022). https://doi.org/10.1016/j.ijepes.2021.107893
R. Babu, S. Raj, B. Dey, B. Bhattacharya, Optimal reactive power planning using oppositional grey wolf optimization by considering bus vulnerability analysis. Energy Convers. Econ. 3(1), 38–49 (2021)
R. Babu, S. Raj, J. Vijaychandra, B.R.V. Prasad, Allocation of phasor measurement unit using an admissible searching-based algorithm A-star and binary search tree for full interconnected power network observability. Optimal Control Appl. Methods. 43(3), 687–710 (2021)
G. Swetha Shekarappa, S. Mahapatra, S. Raj, Voltage constrained reactive power planning problem for reactive loading variation using hybrid Harris Hawk particle swarm optimizer. Electr. Power Compon. Syst. 49(4–5), 421–435 (2021)
S. Raj, B. Bhattacharyya, Optimal placement of TCSC and SVC for reactive power planning using Whale optimization algorithm. Swarm Evol. Comput. 40, 131–143 (2018)
M.S. Shaikh, C. Hua, M. Hassan, S. Raj, M.A. Jatoi, M.M. Ansari, Optimal parameter estimation of overhead transmission line considering different bundle conductors with the uncertainty of load modeling. Optimal Operat. Controls Power Grid 43(3), 652–666 (2021)
G.S. Shekarappa, S. Mahapatra, S. Raj Voltage Constrained Reactive Power Planning by Ameliorated HHO Technique, in Recent Advances in Power Systems. Lecture Notes in Electrical Engineering, vol. 699, (2021)
S. Raj, B. Bhattacharyya, Reactive power planning by opposition-based grey wolf optimization method. Electr. Energy Syst. 28(6), e2551 (2018)
S.K. Panda, P. Ray, Analysis and evaluation of two short-term load forecasting techniques. Int. J. Emerg. Electr. Power Syst. (2021). https://doi.org/10.1515/ijeeps-2021-0051
M. Lamani, Electrical load-temperature CNN for residential load forecasting. Energy 227, 120480 (2021)
L.X. **n Wang, Jerry M. Mendel, Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22(6), 1414–1427 (1992)
B.P. Sahoo, S. Panda, Improved grey wolf optimization technique for fuzzy aided PID controller design for power system frequency control. Sustain. Energy Grids Netw. 16, 278–299 (2018)
V.H. Hinojosa, A. Hoese, Short-term load forecasting using inductive reasoning and evolutionary algorithms. IEEE Trans. Power Syst. 25(1), 565–574 (2010)
S.S. Reddy, Bat algorithm-based backpropagation approach for short-term load forecasting considering weather factors. Electr. Eng. 100(3), 1297–1303 (2018)
S.R. Salkuti, Short-term electrical load forecasting using radial basis function neural networks considering weather factors. Electr. Eng. 100(3), 1985–1995 (2018)
“Collection of weather data” www.timeanddate.com/weather/india/Jaipur
“Collection of load data” State Load Dispatch and Communication Centre, Rajasthan Vidyut Parasaran Nigam www.timeanddate.com/weather/india/Jaipur
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Panda, S.K., Ray, P. Fuzzy Inference Model for Short-Term Load Forecasting. J. Inst. Eng. India Ser. B 103, 1939–1948 (2022). https://doi.org/10.1007/s40031-022-00809-4
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DOI: https://doi.org/10.1007/s40031-022-00809-4