PSO Tuned ANFIS Model for Short Term Photovoltaic Power Forecasting
Harendra Kumar Yadav1, Yash Pal2, Madan Mohan Tripathi3
1Harendra Kumar Yadav, School of Renewable Energy and Efficiency, NIT, Kurukshetra (Haryana), India.
2Yash Pal, Department of Electrical Engineering, NIT, Kurukshetra (Haryana), India.
3Madan Mohan Tripathi, Department of Electrical Engineering, DTU, (New Delhi), India.
Manuscript received on 28 March 2019 | Revised Manuscript received on 09 April 2019 | Manuscript Published on 18 April 2019 | PP: 937-942 | Volume-7 Issue-6S March 2019 | Retrieval Number: F03910376S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Solar power is a green and abundant renewable energy source. Photovoltaic (PV) power forecasting is very essential for the integration of PV power generation to a competitive electricity grid. The production of photovoltaic energy depends to a great extent on solar radiation, which is inherently irregular, so the forecast of photovoltaic energy is necessary for the operation, stability, and reliability of the grid. This article presents a new hybrid approach that combines particle swarm optimization (PSO) and Adaptive neuro-fuzzy inference system (ANFIS) for predicting photovoltaic power. PSO is used to optimize ANFIS parameters. The proposed PSO-ANFIS approach is evaluated on the grid-connected PV power plant data situated in Ghaziabad India. The proposed approach performance is compared with some reported approach and performs better. The computational complexity of the proposed method also appreciable as compared with the other reported methods.
Keywords: Solar Irradiation, PV Power Forecasting, PSO and ANFIS.
Scope of the Article: Wireless Power Transmission