Real-Time Electricity Demand Forecasting

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Real-Time Electricity Demand Forecasting
QING-GUO WANG Distinguished Professor
Institute of Intelligent Systems
(IIS) University of Johannesburg (UJ)
Feb 23, 2016
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Outline

• Motivation
• Popular Models Simulation Results
• Refinements Simulation Results
• Modeling with Weather Data
• Applications
• Conclusions

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Motivation

• Accurate electricity demand forecasting for
  certain leading time is vitally important for the
  power system scheduling and operating and
  satisfaction of consumers.

• Electricity demand is a time-sequence signal with
  evident seasonal patterns property, but some
  factors such as weather, public holidays etc.
  will affect the electricity demand.

• The common electricity demand forecasting models
  could be divided as time-series models,
  regression models, decomposition models and ANN
  models etc.

• Singapore is a typical tropical nation which is
  different from many other countries, thus the
  research of electricity demand forecasting in
  Singapore has special significances.

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Singapore in the World
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Motivation

• This is the electricity demand of Singapore over
  2004-2014.

Electricity demand time series of Singapore (Jan,
2004-Jan, 2014) with half hourly sampling time.
Partial electricity demand time series of
Singapore in normal days.
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Popular Models

• 1. Naive Model

• In naive model, current demand is forecasted by
  the same time of last seasonal period, and its
  general form is

where and are the forecasting
demand and actual demand at time , and is
the selected seasonal pattern length.

• 2. HWT Exponential Smoothing Model

• The triple HWT model is descripted by the
  following equations

where is the smoothed level electricity
demand, and is the trend of electricity
demand. are the seasonal terms of
daily pattern, weekly pattern and yearly pattern,
are respective smoothing parameters.
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Popular Models

• 3. Autoregressive Moving-average (ARMA) Model

• ARMA model is a general class of forecasting
  model that uses lags in the series
  (auto-regressive terms) and/or forecast errors
  (moving-average terms) to perform the prediction.

where and are the level term and trend
term respectively. is the lag operator
are
polynomial functions of backshift operator of
order
respectively is the while noise.

• 4. Artificial Neural Network (ANN)

• ANN is a powerful nonlinear approximator and
  widespread used in time series modeling and
  forecasting.

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Simulation Results with Popular Models

• To evaluate the proposed models, the forecasting
  accuracy is evaluated by the absolute percentage
  error (APE) and the maximal absolute percentage
  error (MAPE) which are given by

where m is No. of specific days with largest
demand forecasting error.
The results are shown below
APE and MAPE of demand forecasting in naive model
APE and MAPE of demand forecasting in HWT model
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Simulation Results with Popular Models
Simulation results
APE and MAPE of demand forecasting in ARMA model
APE and MAPE of demand forecasting in ANN model
Conclusion From the four simulation results, the
triple-seasonal HWT model yields the least APE
error.
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Refined HWT Model

• Refine the HWT model to yield better forecasting
  results.

Refinement procedures

• Before next-day demand forecasting, the actually
  current demand value and its forecasting error
  would have been known, therefore this latest
  forecasting error can be used to improve the
  model forecasting accuracy.

Old forecasting
New forecasting
and the parameters
as well as are selected together
through GA algorithm.
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Refined HWT Model

• Results of refinement.

This refinement brings forecasting accuracy
improvement.
APE and MAPE of prediction of refined HWT model.
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Room for better modeling

• Modeling with weather data

• In Singapore, almost 50 of the total electricity
  demand is used for cooling air-conditioning.

• The temperature, humidity, cloud, wind etc. are
  the most important weather factors that affect
  electricity demand

• Thus electricity demand forecasting with weather
  data will increase the forecasting accuracy.

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Model with Weather Data Method

• Suppose that the electricity forecasting errors
  is partially due to the variation of the weather,
  so the current weather data (as input) and the
  forecasting errors (as output) are trained with
  neural network.

• Further, the future weather data (weather
  forecasting) are used in neural network model

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Model with Weather Data Results
This model with weather data reduces forecasting
errors within 1.
APE and MAPE of prediction of refined HWT model.
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NRF Energy Innovation Research Programme Power
Generation Grant Call 2013
An Integrated Solution for Optimal Generation
Operation Efficiency Through Dynamic Economic
Dispatch
Lead PI Prof. Wang Qing-Guo Institution/Coy/Or
g National University of Singapore Collaborator
s Mr Tan Kok Poh , YTL PowerSeraya Dr
Liu Jidong, YTL PowerSeraya Dr Yu Ming,
Power Automation Project Duration 36
months Funding
gt2,000,000
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OBJECTIVES

• The proposed integrated solution maximizes
  efficiency/profitability of power
  generation/supply while meeting the demand in
  real time.
• Technically, we develop a true dynamic economic
  dispatch (DED) formulation for unknown loads, its
  solution and field implementation. With real time
  sensing, and both supply and demand modelling,
  the optimization will produce the optimal plant
  operation modes and settings which give the
  highest efficiency of the overall system and are
  fed back to the plants for actual execution. It
  will also monitor plant conditions in real time.
• Economically, the highly scalable and innovative
  condition monitoring and control solution for
  Gencos will enable a spin-off from the project to
  monetize through extensive commercialization in
  global markets. Apparently, the efficiency
  solution of this project is applicable to YTL
  PowerSeraya as well as other GenCos and has great
  technical and commercial values.
• Main deliverable is a sophisticated integrated
  solution system for optimal real time dynamic
  bidding and load dispatch, which currently does
  not exist.

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APPROACH
 
 
 

• subject to
• demand constraint
• the operation constraints in terms of
  inequalities.
• two dynamic equations governing transients of
  supply (plants) and demand (market), respectively.

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PowerGenGC ltProject Title, PIgt
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Technical Methods

• Dynamic demand modelling data mining dominates
  in the literature, while we present a new method
  with multiple resolutions/physical
  variables/stochastic input selection methods to
  forecast both energy/heat demands and their price
  changes over time.
• Dynamic plant modelling the steady state models
  are in the literature, while we propose a hybrid
  modelling to obtain dynamic plant models by
  adding dynamics to the first principle static
  models and estimating both state and parameters
  together.
• DED solution method we develop a new iterative
  algorithm for solving this mixed integer
  programing problem. In each iteration, use some
  convex optimization technique when some
  parameters fixed and some search techniques to
  update the values of these parameters.
• Real time implementation with big data
  technology.
• Field testing at power plants to show actual
  energy efficiency/profit enhancement.

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PowerGenGC ltProject Title, PIgt
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Building Efficiency and Sustainability in the
Tropics (SinBerBEST)Costas J. SpanosAndrew S.
Grove Distinguished Professor and Chair, Dept of
EECS, UC Berkeley
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Conclusions

• The electricity demand forecasting for Singapore
  with different typical models are compared, and
  it is found that the HWT model yields the least
  absolute percentage error.
• The HWT model is refined with error feed to
  achieve better forecast accuracy.
• The weather data is used in ANN based error
  modeling to give the best forecast accuracy
• The demand modeling has significant applications
  in both generation and consumption sides.

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• Thank you!

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