LOAD FORECASTING Amanpreet Kaur, CSE 291 Smart Grid Seminar

Outline Introduction Motivation Types Factors Affecting Load Inputs Methods Forecast Algorithm Example

Load fo recas t ing is way o f estimating what future electric load will be for a given forecast horizon based on the available information about the state of the system.

CAISO Outlook

http://www.caiso.com/outlook/SystemStatus.html

Motivation

Operation and planning by ISOs and

utility companies

Trading in electricity market

Load following

Real time dispatch

Operating Reserves

Smart Grid- Automation and Control

Types

Short Term Load Forecast ( one hour -1 week)

Mid Term Load Forecast ( a week - 1 month)

Long Term Load Forecast (month - years )

Independent System Operator e.g. ERCOT

Utility e.g. SDG&E

Organization e.g. UCSD

Building e.g. SDSC

Forecast Horizon

System

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan2

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7 x 104

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan1.5

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Hour-Ahead Market Load for the California Independent System Operator Region Amanpreet Kaur, Hugo T.C. Pedro and Carlos F. M. Coimbra

Jacobs School of Engineering, University of California, San Diego !

Introduction!Electric load forecasting is one of the key elements in decision making, allocating reserves, management of ancillary services, balancing and operation of the electric grid. There has been continuous research in improving these forecasts using various time series, regression and machine learning methods. In this study, we introduce an ensemble forecast method for Hour-Ahead Market (HAM) optimized for each hour of the day. The models used in this ensemble forecast are based solely upon Day-Ahead Market (DAM) forecast. Hour ahead forecasts show substantial improvement over the existing HAM forecast provided by California Independent System Operator (CAISO). An analysis on the various regression and machine learning methods used for an ensemble forecast and the associated error quality is presented. The same methodology used for producing CAISO HAM models is applied to the ERCOT control region, which shows the robustness of the methodology.

ERCOT!

Forecast Model !

Demand load profile of the region operated by CAISO for the year 2011. The load demand varies from 15 GW to 41 GW over the year.

This work was made possible with funding from a California Public Utilities Commission California Solar Initiative RD&D award (CPUC CSI RD&D III) and CEC RESCO.

HAM load forecast using Hourly Ensemble Forecast. The real t ime forecast is avai lable at co imbra.ucsd.edu/forecasting_plots/CAISO_HAM.php

Autocorrelation and Partial Autocorrelation between the residuals of HAM from CAISO, Ensemble (LS-Hourly, LS-Weekly and LS-Models) and ANN model. The blue line shows 95% confidence interval. Except CAISO, residuals from other models are almost white which validates that the forecasting model is capturing all the information in the provided time series about the dynamics of the system load.

HAM load forecast using Hourly Ensemble Forecast. The above forecast is for a week starting from Thursday to Wednesday of April 2013.

Acknowledgements!

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CAISO!

HAM MAE MBE RMSE R2 MAPE ERCOT 512.07 -4.08 694.97 0.97 1.68 LS-Hourly 363.12 10.12 487.16 0.99 1.27

Demand load profile for ERCOT region for the year 2011 varying between 20 GW to 70 GW. As compared to CAISO, there is high demand even in the months of January and February.

Model MAPE

CAISO 1.52

LS-Hourly 0.83

LS-Model 0.56

LS-Weekly 0.58

Block diagram of the forecast model. Day-Ahead Market forecast (DAM) is used as an input and hour ahead forecast ( ) for each model is produced. Forecast of the various models are combined using weights ( ) for each model computed by Least Square (LS) optimization on either hourly, weekly or model basis. The final ensemble forecast ( ) is a combination of various forecast computed as follows

An improved methodology to compute hour ahead forecasts for two major Independent System Operators (ISO) i.e. CAISO and ERCOT has been shown. Since 16 March 2013 till now, there has been 40% improvement in the forecast. The HAM forecasts are available in real-time for CAISO and the work is in progress to implement it for ERCOT and other ISOs.

f1,1 f1,2 f1,nf2,1 f2,2 f2,n...

.... . .

...fm,1 fm,2 fm,n

w1w2...wn

=F1F2...Fn

fi,jwi

Fi

DAM

ARX ARMAX Box-Jenkins Polynomial Model Non-linear ARX Non-linear HW

Least Square

HAM

Background image courtesy to 2012 National Electric Transmission Congestion Study by Office of Electricity Delivery & Energy Reliability [http://energy.gov/oe/services/electricity-policy-coordination-and-implementation/transmission-planning/2012-national].

Courtesy to CAISO and ERCOT operation region image to FERC[http://www.ferc.gov/industries/electric/indus-act/rto.asp].

Power Load and Generation Forecasting

For High Solar Penetration Communities Amanpreet Kaur and Carlos F. M. Coimbra

Jacobs School of Engineering, University of California, San Diego

Introduction!UC San Diego and UC Merced meet substantial parts of their annual energy demand from highly variable solar power plants. High solar penetration communities emulate the future grid scenario for CA, which aims at 33% renewable energy penetration by 2020. The major challenge with this level of penetration of renewable resources is that the variability in solar power production has direct impact on the load demand from the grid, especially during diurnal periods when energy prices and demand are at their peaks. In this research we present integrated load/generation forecasting methodologies and the associated economic gains for such communities.

UC Merced! Applications!

UC San Diego !

Load Shifting: Moving the TES load closer to the off-peak hours, thus reducing high demand charges incurred due to peak time charges.

Load profile for UCSD campus for the year 2010 with approximately 1-2% solar penetration.

15-minute ahead load forecast and power load for the UCSD campus for every 15-minute time interval. This type of load forecast is critical for deploying Automated and Fast Demand Response (ADR/FDR) strategies.

Load profile for UCM campus for the year 2011 with 15-20% solar penetration. The UCM campus has a unique load shape because it has currently shifted the majority of its HVAC load (Central Plant) to the nighttime using Thermal Energy Storage (TES). With the addition of 1MWp Photovoltaic plant (PO), the load shape, i.e. the net power demand from the grid- supplied by utility company (PG&E), is inverted in shape as compared to UCSD load shape.

Funding for this project was provided in part by CEC RESCO and CITRIS grants.

Day- ahead load forecasting for UCM campus for every 15-minute time interval.

Hour- ahead load forecasting for UCM campus and PV plant for every 15-minute time interval, for a few random days in September 2011.

Proposed energy management system for UCM campus using power load demand and generation forecasting.

Table representing time sensitive pricing for UCM campus.

Saving results for UCM energy management system for the year 2011, assuming perfect forecasts.

Acknowledgements!

Conclusion!Solar penetrations up to 2% has negligible to small effects on campus load profiles, but when the penetration increases to 15% or more, it adds proportional variability to the load profile which is challenging to forecast at many temporal horizons of interest. Application of load forecasting for UCM campus with a 1 MWp solar and an average load of 3.5 MWp is shown to work successfully with estimated savings of $50,000/year at current prices.

03/19 03/20 03/21 03/22 03/23 03/24 03/2590

100

110

120

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Load

[kW

]

Minute Data15Minute Average Data

Power Load and Generation Forecasting

For High Solar Penetration Communities Amanpreet Kaur and Carlos F. M. Coimbra

Jacobs School of Engineering, University of California, San Diego

Introduction!UC San Diego and UC Merced meet substantial parts of their annual energy demand from highly variable solar power plants. High solar penetration communities emulate the future grid scenario for CA, which aims at 33% renewable energy penetration by 2020. The major challenge with this level of penetration of renewable resources is that the variability in solar power production has direct impact on the load demand from the grid, especially during diurnal periods when energy prices and demand are at their peaks. In this research we present integrated load/generation forecasting methodologies and the associated economic gains for such communities.

UC Merced! Applications!

UC San Diego !

Load Shifting: Moving the TES load closer to the off-peak hours, thus reducing high demand charges incurred due to peak time charges.

Load profile for UCSD campus for the year 2010 with approximately 1-2% solar penetration.

15-minute ahead load forecast and power load for the UCSD campus for every 15-minute time interval. This type of load forecast is critical for deploying Automated and Fast Demand Response (ADR/FDR) strategies.

Load profile for UCM campus for the year 2011 with 15-20% solar penetration. The UCM campus has a unique load shape because it has currently shifted the majority of its HVAC load (Central Plant) to the nighttime using Thermal Energy Storage (TES). With the addition of 1MWp Photovoltaic plant (PO), the load shape, i.e. the net power demand from the grid- supplied by utility company (PG&E), is inverted in shape as compared to UCSD load shape.

Funding for this project was provided in part by CEC RESCO and CITRIS grants.

Day- ahead load forecasting for UCM campus for every 15-minute time interval.

Hour- ahead load forecasting for UCM campus and PV plant for every 15-minute time interval, for a few random days in September 2011.

Proposed energy management system for UCM campus using power load demand and generation forecasting.

Table representing time sensitive pricing for UCM campus.

Saving results for UCM energy management system for the year 2011, assuming perfect forecasts.

Acknowledgements!

Conclusion!Solar penetrations up to 2% has negligible to small effects on campus load profiles, but when the penetration increases to 15% or more, it adds proportional variability to the load profile which is challenging to forecast at many temporal horizons of interest. Application of load forecasting for UCM campus with a 1 MWp solar and an average load of 3.5 MWp is shown to work successfully with estimated savings of $50,000/year at current prices.

ERCOT UCSD

UCM SDSC

Factors affecting Load Weather Time Economic Random

Time series of CAISO hourly (2009 2012) and daily peak load (2012). Heinemann, G. T.; Nordmian, D. A.; Plant, E. C., "The Relationship Between Summer Weather and Summer Loads

- A Regression Analysis," Power Apparatus and Systems, IEEE Transactions on , vol.PAS-85, no.11, pp.1144,1154, Nov. 1966

Gross, G.; Galiana, F.D., "Short-term load forecasting," Proceedings of the IEEE , vol.75, no.12, pp.1558,1573, Dec. 1987

Inputs Meteorological forecast e.g.

Temperature, Relative Humidity, Wind Speed, Dew Point, etc.

Type of day e.g. Weekday, Holiday, Festival, etc.

Time of the day

Hippert, H.S.; Pedreira, C.E.; Souza, R.C., "Neural networks for short-term load forecasting: a review and evaluation," Power Systems, IEEE Transactions on , vol.16, no.1, pp.44,55, Feb 2001

Methods

Regression

Stochastic Time Series

Fuzzy Logic

Artificial Intelligence/ Machine Learning

Hybrid H. K. Alfares and M. Nazeeruddin, Electric load forecasting: literature survey and classification of methods, International

Journal of System Science, vol. 33, no. 1, pp. 2334, 2002. L. Suganthi and A. A. Samuel, Energy models for demand forecasting review, Renewable and Sustainable Energy Reviews,

vol. 16, no. 2, pp. 1223 1240, 2012.

Stochastic Time Series AutoRegressive Model (AR)

AutoRegressive Model with eXogenous Input (ARX) Non-Linear ARX

AutoRegressive Moving Average (ARMA) ARMAX

ARIMA (Seasonal Modeling) State Space Models e.g. Kalman Filter

Gross, G.; Galiana, F.D., "Short-term load forecasting," Proceedings of the IEEE , vol.75, no.12, pp.1558,1573, Dec.

1987 Taylor, J.W.; McSharry, P.E., "Short-Term Load Forecasting Methods: An Evaluation Based on European Data," Power

Systems, IEEE Transactions on , vol.22, no.4, pp.2213,2219, Nov. 2007 Carter C.K. and Kohn R., On Gibbs sampling for state space models Biometrika (1994) 81(3): 541-553 doi:10.1093/

biomet/81.3.541

Fuzzy Logic It is many valued logic

that approximates the expected values

Disadvantage: Rules for fuzzy logic are determined experimentally with hit and trial

Proposed Solution: Using optimization techniques like Simulated Annealing, GA and ANN model functions.

Papadakis, S.E.; Theocharis, J.B.; Kiartzis, S. J.; Bakirtzis, A.G., "A novel approach to short-term load forecasting using fuzzy neural networks," Power Systems, IEEE Transactions on , vol.13, no.2, pp.480,492, May 1998

Mori, H.; Kobayashi, H., "Optimal fuzzy inference for short-term load forecasting," Power Industry Computer Application Conference, 1995. Conference Proceedings., 1995 IEEE , vol., no., pp.312,318, 7-12 May 1995

Artificial Intelligence Artificial Neural Network Advantage: Ability to learn Disadvantage: Over fitting and over parameterizing k Nearest Neighbor Advantage: Good for forecasting load profile for a whole

day or longer period of time Disadvantage: Requires historical data to create a

database.

Hippert, H.S.; Pedreira, C.E.; Souza, R.C., "Neural networks for short-term load forecasting: a review and evaluation," Power Systems, IEEE Transactions on , vol.16, no.1, pp.44,55, Feb 2001

Lora A.C., Riquelme J.C., Ramos J.L.M., Santos J.M.R. , Exposito A.G., Influence of kNN-Based Load Forecasting Errors on Optimal Energy Production, Progress in Artificial Intelligence, Lecture Notes in Computer Science, Volume 2902, 2003, pp 189-203

Expert & Hybrid Models Support Vector Regression (SVR) Self Organizing Map (SOM) Meta Learning Hybrid Model: Combination of various forecasting models Using various optimization techniques like particle swarm, ant colony, GAs, etc. are introduced to optimize the model parameters, input selection and model selection !!

Bo-Juen Chen; Ming-Wei Chang; Chih-Jen Lin, "Load forecasting using support vector Machines: a study on EUNITE competition 2001," Power Systems, IEEE Transactions on , vol.19, no.4, pp.1821,1830, Nov. 2004

Shu Fan; Luonan Chen, "Short-term load forecasting based on an adaptive hybrid method," Power Systems, IEEE Transactions on , vol.21, no.1, pp.392,401, Feb. 2006

Marin Matija, Johan A.K. Suykens, Slavko Krajcar, Load forecasting using a multivariate meta-learning system, Expert Systems with Applications, Volume 40, Issue 11, 1 September 2013, Pages 4427-4437, ISSN 0957-4174.

Load Forecast Algorithm

Data Preprocessing

Model formulation or selection

Identification or updating model parameters

Testing the model performance and updating the forecast

If performance is unsatisfactory return to Step1 or Step2, else return to Step 3

Example: HAM CAISO Forecast using Ensemble Method

http://coimbra.ucsd.edu/forecasting_plots/CAISO_HAM.php?z=4

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan2

3

4

5

6

7 x 104

Load

[MW

]

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan1.5

2

2.5

3

3.5

4

4.5 x 104

Load

[MW

]

Hour-Ahead Market Load for the California Independent System Operator Region Amanpreet Kaur, Hugo T.C. Pedro and Carlos F. M. Coimbra

Jacobs School of Engineering, University of California, San Diego !

Introduction!Electric load forecasting is one of the key elements in decision making, allocating reserves, management of ancillary services, balancing and operation of the electric grid. There has been continuous research in improving these forecasts using various time series, regression and machine learning methods. In this study, we introduce an ensemble forecast method for Hour-Ahead Market (HAM) optimized for each hour of the day. The models used in this ensemble forecast are based solely upon Day-Ahead Market (DAM) forecast. Hour ahead forecasts show substantial improvement over the existing HAM forecast provided by California Independent System Operator (CAISO). An analysis on the various regression and machine learning methods used for an ensemble forecast and the associated error quality is presented. The same methodology used for producing CAISO HAM models is applied to the ERCOT control region, which shows the robustness of the methodology.

ERCOT!

Forecast Model !

Demand load profile of the region operated by CAISO for the year 2011. The load demand varies from 15 GW to 41 GW over the year.

This work was made possible with funding from a California Public Utilities Commission California Solar Initiative RD&D award (CPUC CSI RD&D III) and CEC RESCO.

HAM load forecast using Hourly Ensemble Forecast. The real t ime forecast is avai lable at co imbra.ucsd.edu/forecasting_plots/CAISO_HAM.php

Autocorrelation and Partial Autocorrelation between the residuals of HAM from CAISO, Ensemble (LS-Hourly, LS-Weekly and LS-Models) and ANN model. The blue line shows 95% confidence interval. Except CAISO, residuals from other models are almost white which validates that the forecasting model is capturing all the information in the provided time series about the dynamics of the system load.

HAM load forecast using Hourly Ensemble Forecast. The above forecast is for a week starting from Thursday to Wednesday of April 2013.

Acknowledgements!

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CAISO!

HAM MAE MBE RMSE R2 MAPE ERCOT 512.07 -4.08 694.97 0.97 1.68 LS-Hourly 363.12 10.12 487.16 0.99 1.27

Demand load profile for ERCOT region for the year 2011 varying between 20 GW to 70 GW. As compared to CAISO, there is high demand even in the months of January and February.

Model MAPE

CAISO 1.52

LS-Hourly 0.83

LS-Model 0.56

LS-Weekly 0.58

Block diagram of the forecast model. Day-Ahead Market forecast (DAM) is used as an input and hour ahead forecast ( ) for each model is produced. Forecast of the various models are combined using weights ( ) for each model computed by Least Square (LS) optimization on either hourly, weekly or model basis. The final ensemble forecast ( ) is a combination of various forecast computed as follows

An improved methodology to compute hour ahead forecasts for two major Independent System Operators (ISO) i.e. CAISO and ERCOT has been shown. Since 16 March 2013 till now, there has been 40% improvement in the forecast. The HAM forecasts are available in real-time for CAISO and the work is in progress to implement it for ERCOT and other ISOs.

f1,1 f1,2 f1,nf2,1 f2,2 f2,n...

.... . .

...fm,1 fm,2 fm,n

w1w2...wn

=F1F2...Fn

fi,jwi

Fi

DAM

ARX ARMAX Box-Jenkins Polynomial Model Non-linear ARX Non-linear HW

Least Square

HAM

Background image courtesy to 2012 National Electric Transmission Congestion Study by Office of Electricity Delivery & Energy Reliability [http://energy.gov/oe/services/electricity-policy-coordination-and-implementation/transmission-planning/2012-national].

Courtesy to CAISO and ERCOT operation region image to FERC[http://www.ferc.gov/industries/electric/indus-act/rto.asp].

f1,1 f1,2 f1,nf2,1 f2,2 f2,n...

.... . .

...fm,1 fm,2 fm,n

w1w2...wn

=F1F2...Fn

Error Analysis

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QUESTIONS ? Thanks !