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### Five most used load forecasting models

1. 1. FIVE MODELS TRADITIONALLY USED FOR ELECTRICAL LOAD PREDICTIONS DR.MRINMOY MAJUMDER (ORCID ID : 0000-0001-6231-5989) “RENEWABLE ENERGY” of MTECH(HYDROINFORMATICS ENGG “ INNOVATE FOR SUSTAINABILITY”- www.baipatra.ws
2. 2. IMPORTANCE Load forecasting helps an electric utility to make important decisions including decisions on purchasing and generating electric power, load switching, and infrastructure development. Load forecasts are extremely important for energy suppliers, ISOs, financial institutions, and other participants in electric energy generation, transmission, distribution, and markets. Most forecasting methods use statistical techniques or artificial intelligence algorithms such as regression, neural networks, fuzzy logic, and expert systems.
3. 3. DEFINITION Prediction of load or generation requirement can be performed to assist in operation planning, expansion strategy and policy formulations. Hydropower plants has a gestation period of 10 to 15 years Forecasting of load can be done for three different duration based on accuracy required and circumstances prevailing. Models can be conceptual or statistical depending on the requirement.
4. 4. Type of Forecasting Models LOAD PREDICTION MODEL TYPES SHORT TERM MEDIUM TERM LONG TERM COVERING A PERIOD OF 20 YEARS OR MORE COVERING A PERIOD OF 8 TO 10 YEARS COVERING A PERIOD OF 4 TO 5 YEARS PURRPOSE : OPERATION PLANNING PURRPOSE : BASIS FOR EXPANSION PURRPOSE : POLICY FORMULATION
5. 5. Model One : SCHEER FORMULA G = 10 𝐶 𝑈0⋅15 WHERE G = ANNUAL GROWTH IN GENERATION(PER CENT) U = PER CAPITA GENERATION C = CONSTANT = 0.02(POPULATION GROWTH RATE)+1.330 THE CONSTANT IN THE SUPERSCRIPT OF U IS ASSUMED AS PER THE THUMB RULE THAT ‘A HUNDRED FOLD INCREASE IN U WILL REDUCE THE RATE OF GROWTH BY HALF’ LONG TERM LOAD FORECASTING MODEL
6. 6. POINTS TO REMEMBER MAGNITUDE OF FOLLOWING PARAMETERS ARE REQUIRED : •GENERATION OF THE STARTING YEAR IS •POPULATION OF STARTING YEAR •POPULATION OF THE CONSEQUENT YEARS 1 IF THE VALUE OF ABOVE PARAMETERS IS KNOWN THEN U AND C CAN BE CALCULATED FROM WHICH THE GENERATION REQUIREMENT OF THE STARTING YEAR IS DERIVED FROM THE SCHEER FORMULA 2 THE RATE OF GROWTH IN THE NEXT YEAR IS CALCULATED BY MULTIPLYING THE GROWTH RATE OF STARTING YEAR WITH (1+G)/100. 3 FROM THE GROWTH RATE OF SECOND YEAR AND THE POPULATION OF THE SAME YEAR AS KNOWN FROM THE FIRST POINT THE U AND C OF SECOND YEAR CAN BE PREDICTED FROM WHICH RATE OF GROWTH CAN BE ESTIMATED BY THE SAME FORMULA. 4 IN THIS WAY YEAR BY YEAR PREDICTION OF GENERATION REQUIREMENT IS DETERMINED. 5
7. 7. Model two : BELGIUM FORMULA E = 𝐾 × 𝑀0⋅6 × 2 0.465𝑡 WHERE E = ELECTRICITY CONSUMPTION M = INDEX OF MANUFACTURE OF PRODUCTION t = TIME FOR WHICH CONSUMPTION TO BE PROJECTED K = ADJUSTMENT FACTOR LONG TERM LOAD FORECASTING MODEL
8. 8. POINTS TO REMEMBER THIS FORMULA IS AN EMPIRICAL FORMULA FOR EXTRAPOLATION OF TREND OF ENERGY CONSUMPTION DIFFERENT COUNTRIES HAVE SEPARATE FORMULA FOR THE ESTIMATION OF ENERGY CONSUMPTION THE ACCURACY OF THE FORMULA DEPENDS ON THE CALIBRATION AND MAY VARY WITH TYPE OF DATA AVAILABLE AND OTHER RELATED APPROXIMATIONS.
9. 9. Model Three : Additive Model by Chen et.al. 𝐿 = 𝐿 𝑛 + 𝐿 𝑤 + 𝐿 𝑠 + 𝐿 𝑟 where L is the total load, Ln represents the “normal” part of the load, which is a set of standardized load shapes for each “type” of day that has been identified as occurring throughout the year, Lw represents the weather sensitive part of the load, Ls is a special event component that create a substantial deviation from the usual load pattern, and Lr is a completely random term, the noise. LONG TERM LOAD FORECASTING MODEL
10. 10. Model Four : Multiplicative Model 𝐿 = 𝐿 𝑛 × 𝐹𝑤 × 𝐹𝑆 × 𝐹𝑟 where Ln = the normal(base) load Fw, Fs, and Fr are positive numbers representing the corrections based on current weather (Fw), special events (Fs), and random ﬂuctuation (Fr). Factors like electricity pricing (Fp) and load growth (Fg) can also be included. LONG TERM LOAD FORECASTING MODEL
11. 11. Model Five : Feinberg’s Load Forecasting Model 𝐿 𝑡 = 𝐹𝑎 + 𝑅𝑡 where Fa is 𝐹 ⅆ 𝑡 ⋅ ℎ 𝑡 ⋅ 𝑓 𝑤𝑡 L(t) is the actual load at time t, d(t) is the day of the week, h(t) is the hour of the day, F(d,h) is the daily and hourly component, w(t) is the weather data that include the temperature and humidity, f(w) is the weather factor, R(t) is a random error. And f(w) is estimated by : 𝑓 𝑤 = 𝛽0 + 𝛽𝑗 𝑋𝑗where Xj are explanatory variables which are nonlinear functions of current and past weather parameters and βo, βj are the regression coefﬁcients. MID TERM LOAD FORECASTING MODEL
12. 12. Types of Models used in Short Term load forecasting Similar-day approach Historical data for days within the last one to three years are searched for similar characteristics for forecasting the data of a similar day or season. Regression methods Various regressive techniques are used to model the relationship between load consumption and other factors such as weather, day type, and customer class. Time series Time series Models depends on the assumption that the data have an “internal structure”, represented by the autocorrelation, trend, or seasonal variation. The different methods used in Time Series Modelling is: ARMA (autoregressive moving average), ARIMA (autoregressive integrated moving average), ARMAX (autoregressive moving average with exogenous variables), and ARIMAX (autoregressive integrated moving average with exogenous variables).
13. 13. Contd. Neural networks The artiﬁcial neural networks (ANN) has been widely used for electric load forecasting since 1990. Expert systems “Rule based forecasting” uses mostly heuristic rules of nature for accurate forecasting. Expert systems, incorporates the rules and procedures used by human experts into a software framework for automatically making forecasts without human assistance.
14. 14. Resources and References 1.E. A. Feinberg and D.Genethliou.Load Forecasting, Applied Mathematics for Power Systems. 2.E.A. Feinberg, J.T. Hajagos, and D. Genethliou. Load Pocket Modelling. Proceedings of the 2nd IASTED International Conference: Power and Energy Systems, 50–54, Crete, 2002. 3.E.A. Feinberg, J.T. Hajagos, and D. Genethliou. Statistical Load Modelling. Proceedings of the 7th IASTED International Multiconference: Power and Energy Systems, 88–91, Palm Springs, CA, 2003. 4.H. Chen, C.A. Canizares, and A. Singh. ANN-Based Short-Term Load Forecasting in Electricity Markets. Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, 2:411–415, 2001. 5.M.M. Dandekar and K.N.Sharma. Water Power Engineering, Vikas Publishing House Pvt Ltd.,1979.