An Integrated Access to Electricity Price Forecasting using K Means based ANN
Anamika
Anamika, Member IEEE, Department of Electrical & Electronics Engineering, Galgotia College of Engineering and Technology, Greater Noida (Uttar Pradesh), India.
Manuscript received on 25 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 1677-1681 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B13040782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1304.0782S319
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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: Mid-time period strength market Clearing charge (MCP) looking forward to is some days beforehand forecasts for each day facts. It has ended up being vital for better implementation of asset appropriation, making plans, respective contracting and arranging reasons for a strength exhibit. in this paper, an integrated midterm strength MCP estimating version is proposed to foresee the hourly MCPs for an entire month. The proposed model incorporates a k manner bunching module and artificial Neural network (ANN) guaging module. The ok way bunching module is applied to signify the 24 hours of multi day into some gatherings dependent on the closeness in cost. After the association, a Multi Layered Perceptron (MLP) is used to gauge the fee esteems in every one of the gatherings. to check the exactness of the proposed version the imply Absolute percent error (MAPE) and relapse coefficients are resolved for each one of the gatherings. Trial outcomes making use of recorded records from the Indian power Markets showed that the proposed included anticipating version can enhance the expectation exactness of price esteems and ultimately enhance the overall framework exhibitions.
Keywords: Marketplace Clearing Charge, Artificial Neural Networks, ok Method Bunching.
Scope of the Article: Broadband Access Technologies