1) The document discusses using an artificial neural network for load forecasting in smart grids. It outlines the objectives, existing forecasting methods, and advantages of using artificial neural networks (ANNs). 2) It proposes using different types of ANNs including feed-forward and feedback networks. It describes how to structure, train, and optimize ANNs using backpropagation. 3) The results section shows the ANN was able to accurately forecast load based on historical load and weather data from Ontario, Canada. It concludes that more neurons and additional training data can improve forecasting accuracy for smart grid load forecasting.

1. ARTIFICIAL NEURAL NETWORK
FOR LOAD FORECASTING IN
SMART GRID
Ehsan Zeraatparvar
RMRP Course

2. Outline
 Introduction and Background
 Objectives
 Load Forecasting Methods
 Why ANN?
 Proposed Approach
 What Are Neural Networks
 Different Types of Neural Networks
 Network Structure
 Training a Neural Network
 Back Propagation
 Simulation Results
 Training Performances
 Result Comparison
 Conclusions

3. Introduction and Background
 Objective
 Electric power generation, transmission, distribution, security
 Increase or decrease output of generators
 Interchange power with neighboring systems
 Prevent overloading
 Electric power market
 Price settings
 Economic operation of power plants

4. Introduction and Background
 Load Forecasting Methods
 Parametric methods
 Artificial intelligence methods
 Artificial neural networks
Feed-forward network
Feedback network
 Fuzzy logic
 Expert systems

5. What Are Neural Networks?
 Massively parallel networks of simple processing elements (neurons)
 Designed to emulate the functions and structure of the brain
 can solve very complex problems
 a new method of programming computers

6. Different Types of Neural Networks
 Feed-forward Network
 Signals travel in one way only; from input to output
 No feedback
 the output of any layer does not affect that same layer
 Feedback Network
 signals traveling in both directions by introducing loops
 Feedback networks are dynamic
 They remain at the equilibrium point until the input changes

7. Network Structure
 Estimate the number of layers and of neurons
 trial and error procedure
 Two types of adaptive algorithms can be used:
 start from a large network
 begin with a small network

8. Network Training
 The process of determining the network parameters to achieve the
desired objective
 Neural networks learn from examples
 The most basic method of training; Trial and Error
 epoch-by-epoch learning
 The Aim: determine a set of weights which minimizes the error

9. Back Propagation
 Most widely and frequently used neural network learning algorithm
 mathematically designed to minimize the error
 and propagate backward the local error terms

10. Simulation Results
 Forecasting Procedure
 Data Source
 Ontario weather stations and dispatching centers
 Historical Data
 Load – load for the year 2008
 Weather – weighted average hourly weather conditions of
stations in Ontario Province, Canada for 2 years

11. Simulation Results
trainlm algorithm performance plot trainbr algorithm performance plot
 Training Performances

12. Result Comparison
 The network test simulation should be made in order to find out the
performance in a real problem:
The simulation result of the trainbr algorithm with 8 neurons

13. Result Comparison
The simulation results of both trainbr and trainlm with 8 neurons
The simulation results of trainlm with 8 neurons and 30 neurons
Test target, Trainlm with 30 neurons, trainlm with 8 neurons

14. Conclusions
 Trainbr is one of the best choices to do load forecast
 More neurons are needed in network structure to obtain
accurate results
 A few of the simulation part didn’t match the real demand
because of lack of information
 More enough information and a precise training give us better
results for load forecasting in a smart grid.

15. Special thanks for your care.