Introduction to Forecasting Dr. Yogesh K. Bichpuriya TCS Research - PowerPoint PPT Presentation

Introduction to Forecasting Dr. Yogesh K. Bichpuriya TCS Research and Innovation TATA Consultancy Services Ltd. Pune, India October 26, 2018 Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 1 / 34

Menu of the day 1 Introduction 2 Load Forecasting 3 Short Term Load Forecasting Load Profiles Forecasting Approach Case Studies 4 Other Forecasting Problems in Power Sector 5 Conclusion Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 2 / 34

Introduction Data Analytics for Smart Grid Descriptive Analytics Predictive Analytics Prescriptive Analytics Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 3 / 34

Introduction Prediction !! Prediction is very difficult, especially if it’s about the future. - Nils Bohr, Nobel laureate in Physics (Source: Walter Baxter, geograph.org.uk) Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 4 / 34

Introduction Introduction to Forecasting 1 Important analytics for many planning and operational decision making in Electricity sector Meteorology Supply industry Tourism sector · · · 2 A well studied branch (and yet evolving) in econometrics, statistics and operational research. 3 One of the first papers on electrical load forecasting is by Godard [Godard, 1955] in the year 1955. 4 Forecasting is even more important and challenging today - it stimulates research in modeling techniques and algorithm development. Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 5 / 34

Introduction Process of Forecasting [Holden and Peel, 1988] summarized the process of making economic forecasts which can be extended for forecasts in general. The process includes the following. 1 Defining a causal relationship to form a model and variables to be included. 2 Selecting a functional form to represent the model. 3 Collection of the data and use it for estimating model parameters. If estimation is not satisfactory, model specifications can be changed. 4 Forecasting of future values of the explanatory variables are determined. 5 Forecasting the variables of interest using the model. 6 The forecasts are examined and adjustments can be made to take account of information not included in the model such as policy changes. Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 6 / 34

Load Forecasting New Wine in Old Bottle? Electric grid is transforming for - Reliability Affordability Sustainability What is changing - Renewable Energy Targets- Solar, Wind Active Prosumers Electric Vehicles Energy Storage Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 7 / 34

Load Forecasting Challenges or Opportunities Scheduling and trading Upgradation of infrastructure to enable efficient coordination and control - to ensure reliability and resilience of the distribution system Development of distribution grid analytics - to monitor and optimize grid operations, to reduce demand-supply imbalances, etc. Strengthen the customer facing side of the Utility to effectively manage the customers’ participation Generation reserves in case of non-availability of the green power Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 8 / 34

Load Forecasting When the going gets tough, the tough get going Missing data and quality of data Projection of explanatory variables e.g., weather, economic indicators Impact of regulatory policies - open access in distribution system Significance of point forecasts or scenario based forecasts in long and medium term Increasing penetration of distributed energy resources (e.g., roof-top solar PV) Increasing number of electrical/electronic appliances, but more energy efficient Electric vehicles - moving and flexible loads We sail within a vast sphere, ever drifting in un- certainty, driven from end to end . - Blaise Pascal Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 9 / 34

Load Forecasting Description of Load forecasting problems Long term Medium term Short term Time hori- Year(s) ahead Month(s) ahead Day(s) ahead zon Quantity Peak/average load Hourly hourly/15-minutes of interest in the forecasted peak/average load (load profile) year load of a day of next day during a forecast month Load pro- Trend component Seasonal and Seasonal compo- file compo- trend components nent sition Important Economic, demo- Weather and eco- Day type and exogenous graphic and devel- nomic indicators weather variables opment indicators Applications Portfolio manage- Portfolio manage- Day ahead trading ment (resource ment (resource and scheduling planning) and planning) network planning Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 10 / 34

Load Forecasting Understanding Trend and Seasonal Components Hourly Maximum Load 1000 800 1050 Trend Component 1000 Load (in MW) 950 900 100 Seasonal Component 0 −100 50 Random Component 0 −50 5 10 15 20 25 30 35 40 45 Month Index Figure: Monthly peak load of a distribution utility Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 11 / 34

Short Term Load Forecasting Load Profiles Load Profiles of Distribution Companies (a) Urban Distribustion Company (b) State Distribution Company Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 12 / 34

Short Term Load Forecasting Load Profiles Load Profiles on Holidays vs Normal Day (c) Independence Day vs Normal Day (d) Holi vs Normal Day Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 13 / 34

Short Term Load Forecasting Load Profiles Load Profiles for Holidays/Calamities (e) Ambedkar Jayanti (Apr. 14) (f) Maharashtra Day (May 1) (g) Terror Attack (Nov. 26, 2008) (h) Cyclone (Nov. 10, 2009) Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 14 / 34

Short Term Load Forecasting Load Profiles Factors affecting Load Pattern Consumer behaviour Consumer category Weather Seasonal factors Economic factors Time factors Type of day Weekdays Weekends Holidays Social/community events - election, strike, etc. Calamities - Natural and man-made Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 15 / 34

Short Term Load Forecasting Forecasting Approach Statistical Analysis of Data Mean-Variance analysis - Coefficient of Variation Check for Stationarity Correlation study Similar days Temperature Vs load Humidity Vs load Rainfall Vs load · · · Outlier/anomalous profiles detection Auto Correlation Function(ACF), Partial Auto Correlation Function (PACF) analysis · · · Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 16 / 34

Short Term Load Forecasting Forecasting Approach Forecasting Methods Regression Models Similar day approach · · · Time Series Models Autoregressive (AR) Moving Average (MA) Autoregressive Moving Average (ARMA) Integrated Autoregressive Moving Average (ARIMA) Generalized Autoregressive Conditional Heteroskedastic (GARCH) Evolutionary and Learning Models Expert System (ES) Genetic Algorithm (GA) Particle Swarm Optimization (PSO) Fuzzy Systems (FS) Artificial Neural Network (ANN) Support Vector Machine (SVM) Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 17 / 34

Short Term Load Forecasting Forecasting Approach Similar Day Approach A load profile can be expressed as a linear combination of reference days’ load profiles i.e. N r ˆ � L = w i × L i i =1 where N r � w i = 1 , w i ≥ 0 i =1 N r - Number of reference days. These reference days can be selected from the historical data using following two approaches. 1 Best correlated days (Correlation coefficient of the two days’ load profiles.) 2 Least distance days (Second norm of difference vector of the two days’ load profiles.) Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 18 / 34

Short Term Load Forecasting Forecasting Approach Similar Day Approach (cont.) To estimate the weights w i , a Least Absolute Value (LAV) problem can be formulated as follows: N r � min | L − w i × L i | i =1 subject to N r � w i = 1 i =1 w i ≥ 0 Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 19 / 34

Short Term Load Forecasting Forecasting Approach Time Series Models Assumption: The time series is stationary [Brockwell and Davis, 2008]. Autoregressive Model- AR(p) z t can be written as weighted sum of past p values of the z ’s , plus an added shock a t z t = φ 1 z t − 1 + φ 2 z t − 2 + · · · + φ p z t − p + a t (1) Moving Average Model-MA(q) z t can be written as weighted sum of present and past q values of the "white noise" process a t z t = a t − θ 1 a t − 1 − θ 2 a t − 2 − · · · − θ q a t − q (2) Autoregressive-Moving Average Model-ARMA(p,q) z t = φ 1 z t − 1 + φ 2 z t − 2 + · · · + φ p z t − p + a t − θ 1 a t − 1 − θ 2 a t − 2 − · · · − θ q a t − q Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 20 / 34

Short Term Load Forecasting Forecasting Approach Time Series Models (cont.) Integrated Autoregressive-Moving Average Model-ARIMA(p,q,d) If the series is not stationary, the series can be transformed into a stationary series by using difference operation. Differenced time series y t can be written as: y t = ▽ d z t Then, y t = φ 1 y t − 1 + φ 2 y t − 2 + · · · + φ p y t − p + a t − θ 1 a t − 1 − θ 2 a t − 2 − · · · − θ q a t − q Yogesh Bichpuriya Introduction to Forecasting October 26, 2018 21 / 34