forecasting Article A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets Radhakrishnan Angamuthu Chinnathambi 1 , Anupam Mukherjee 1 , Mitch Campion 1 , Hossein Salehfar 1 , Timothy M. Hansen 2 , Jeremy Lin 3 and Prakash Ranganathan 1, * 1 Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58203, USA; radhakrishnan.angamu@und.edu (R.A.C.); anupam.mukherjee86@gmail.com (A.M.); mitchell.campion@und.edu (M.C.); hossein.salehfar@engr.und.edu (H.S.) 2 Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57007, USA; Timothy.Hansen@sdstate.edu 3 Transmission Analytics, 2025 Guadalupe St, Suite 260, Austin, TX 78705, USA; Jeremy.lin@transmissionanalytics.net * Correspondence: Prakash.ranganathan@engr.und.edu; Tel.: +1-701-777-4431 Received: 18 June 2018; Accepted: 9 July 2018; Published: 12 July 2018   Abstract: Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods. Keywords: ARIMA-SVM (Support Vector Machine); ARIMA-RF (Random Forest); ARIMA-GLM (Generalized Linear Model); electricity price forecasting; Iberian market; day-ahead price 1. Introduction Electricity price forecasting is a branch of energy forecasting that focuses on predicting the spot and day-ahead prices in the electricity market. Price forecasting is one of the fundamental tasks in utilities and energy trading entities for various decision-making mechanisms, for example, adjusting bids to maximize profits, scheduling outages and establishing load profiles. In particular, more accurate short-term price forecasts benefit both producers and consumers, as they can maximize profit and minimize the cost of a variety of applications such as home energy management programs in dynamic pricing environments and demand response. Electricity price is highly unstable in the open market or for consumers and its instability further increases by the deployment of the smart grid as it is influenced by many visible and invisible factors. For example, short-term price (e.g., hourly scales) depends on current demand, type of energy used for generation, historical price trend, hour of days and so forth. Medium term (weekly scales) and Long-term price (monthly to yearly scales) is influenced by factors like energy reserve (oil and gas), expected demand, population growth and various economic factors. Most of the research on price prediction uses these factors as input features for prediction models. Forecasting 2019, 1, 26–46; doi:10.3390/forecast1010003 www.mdpi.com/journal/forecasting