Smart grids are still under evolution, and different energy utilities (like reliance, tata, powergrid, govt. discoms etc.,) will need different solutions as per their underlying infrastructure, legacy requirements, and business case. however, the underlying technical tools will be same and applicable to all the energy utilities which are moving towards making their grids smart. one of the most important technical tools among them is of forecasting for electricity parameters- electricity price, grid frequency and load (either in wholesale or retail market- a wholesale electricity market exists when competing generators offer their electricity output to retailers. the retailers then re-price the electricity and take it to market (retail)). the developed new technology is broadly pertaining to the forecasting of electricity parameters under smart grid paradigm and price forecasting in-specific. the price forecasting is the key tool for both the retailers and the end-consumers. the forecasted price will be useful for the end-consumers in the retail markets to schedule their loads and for the retailers to procure (bid) power from the wholesale market. a huge amount of saving in the money is possible if proper tool is used. for it to be used in the real-time to forecast prices at the end-consumer premises, it should not be computationally intense and also should be able to embed into the smart meters (for retail segment).