Nodal congestion price and IMO price forecasting in restructure power system market

As human civilization become modernized day after day, requirement of human being increases along with the utilization of electrical power. The way of procurement of electrical power should be more lucrative and customer should able to choose the beneficial deals to procure the same. Previously we found that the electrical market is monopoly in nature and customers/consumers are often uncomfortable to procure electrical power. In respect to enjoy the benefit to all suppliers and consumers, a deregulated competitive open market is required. The competitive market can be constructed by restructuring it by incorporating competitive and profit maximization format. This market can be operated by an independent regulating body which is responsible to maintain the financial and administrative constrains of the market. In every nodal point of an electrical network, Locational marginal price (LMP) is an important parameter which can be estimated by Nodal congestion price (NCP). On the other hand, IMO pay is another crucial criterion in open market power bidding system. This paper introduces a method for forecasting Nodal congestion price (NCP), IMO Pay using ML. The performance of Ridge is outperformed by comparing prediction results with Gradient boosting Regression, Gradient Boosting, Decision tree and LR.