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1. ELECTRICITY
DEMAND
FORECASTING
Deepak Chaurasiya
21EEB0A12

2. Table of Content

3. Definition: Demand forecasting predicts future customer demand for products or services,
aiding businesses in optimizing production, inventory, pricing, and resource allocation
through analysis of historical data, market trends, and other factors.
Related to electrical engineering: Demand forecasting is a critical component of electrical
engineering, involving the prediction of future electricity demand based on historical data,
environmental factors, and other variables.
 Energy Management:
 Demand forecasting enables utilities and energy providers to anticipate and plan for
future electricity demand, ensuring adequate generation capacity and resource
allocation.
 It supports the optimization of energy production, distribution, and storage, minimizing
costs and maximizing efficiency.

4. WHY WE NEED FORECASTING
Optimized Resource Allocation
Integration of Renewable Energy
Demand Response & Energy Efficiency
Infrastructure Planning & Investment
Environmental Impact

5. METHODS AND MODELS
 Demand forecasting employs various methodologies and models to
predict electricity demand accurately.
 These approaches range from traditional statistics to advanced
machine learning.
 Selection depends on data characteristics, forecasting horizon, and
complexity.
 Statistical methods like ARIMA, Exponential Smoothing and machine
learning like neural networks, support vector machines.

6.  Decision Trees:
 A decision tree is one of the most powerful tools of supervised learning algorithms used for
both classification and regression tasks. It builds a flowchart-like tree structure where each
internal node denotes a test on an attribute, each branch represents an outcome of the test,
and each leaf node (terminal node) holds a class label. It is constructed by recursively splitting
the training data into subsets based on the values of the attributes until a stopping criterion is
met, such as the maximum depth of the tree or the minimum number of samples required to
split a node.
 The decision tree operates by analyzing the data set to predict its classification. It commences
from the tree’s root node, where the algorithm views the value of the root attribute
compared to the attribute of the record in the actual data set. Based on the comparison, it
proceeds to follow the branch and move to the next node.
 The algorithm repeats this action for every subsequent node by comparing its attribute values
with those of the sub-nodes and continuing the process further. It repeats until it reaches the
leaf node of the tree.
Machine Learning and Hybrid Methods

7. Machine Learning and Hybrid Methods

8.  Random Forest:
 A random forest is an ensemble learning method that combines the predictions from multiple
decision trees to produce a more accurate and stable prediction. It is a type of supervised
learning algorithm that can be used for both classification and regression tasks.
 Every decision tree has high variance, but when we combine all of them in parallel then the
resultant variance is low as each decision tree gets perfectly trained on that particular sample
sample data, and hence the output doesn’t depend on one decision tree but on multiple
multiple decision trees. In the case of a classification problem, the final output is taken by
by using the majority voting classifier. In the case of a regression problem, the final output is
output is the mean of all the outputs. This part is called
Machine Learning and Hybrid Methods

9.  Random Forest:
Machine Learning and Hybrid Methods

10. Machine Learning and Hybrid Methods
 Neural Networks:
 Suitable for capturing nonlinear patterns and interactions.
 Neural networks consist of interconnected layers of neurons
that process input data through nonlinear activation functions.
 They adjust connection weights between neurons to minimize
forecast errors and optimize performance.
 NNs are widely used for electric load forecasting tasks,
including short-term, medium-term, and long-term predictions.
 They excel at capturing complex patterns and nonlinear
relationships in load data.

11. Machine Learning and Hybrid Methods

12. Machine Learning and Hybrid Methods

13. ADVANTAGES & DISADVANTAGES
ADVANTAGES DISADVANTAGES
Interpretability Limited Flexibility
Robustness Assumption Sensitivity
Historical Trend Analysis Difficulty with Long-Term
Forecasting
ADVANTAGES DISADVANTAGES
Flexibility Black-Box Nature
Prediction Accuracy Data Dependency
Automation Computational Complexity
Statistical Model
Machine Learning Model

14. TYPES OF FORCASTING
 Short-Term Forecasting:
 Predictions made for small time intervals (e.g., hours, days).
 Focus on immediate operational decisions and resource allocation.
 Examples: Hourly load forecasts for grid management and energy trading.
 Medium-Term Forecasting:
 Forecasts cover longer timeframes, typically ranging from weeks to months.
 Used for mid-range planning and capacity expansion decisions.
 Examples: Weekly or monthly load forecasts for infrastructure planning.
 Long-Term Forecasting:
 Predictions made for extended periods, often spanning years.
 Inform strategic planning, policy-making, and investment decisions.
 Examples: Annual load forecasts for long-term energy market analysis and
renewable energy integration.

15. DEMAND SIDE MANAGEMENT
Demand-Side Management (DSM) involves strategies to influence consumer
electricity consumption patterns to optimize grid operation and resource
utilization.
 Load Shifting:
 Encouraging consumers to shift electricity usage from peak to off-peak periods.
 Reduces strain on the grid during peak demand hours and minimizes the need for
expensive peaking power plants.
 Demand Response Programs:
 Incentivizing consumers to adjust their electricity consumption in response to
price signals or grid conditions.
 Energy Efficiency Measures:
 Promoting energy-efficient technologies and practices to reduce overall electricity
demand.

16. A typical all India daily load curve:

17. RENEWABLE ENERGY INTEGRATION
Renewable energy integration involves incorporating variable and
intermittent renewable energy sources, such as solar and wind power,
into the electricity grid.
 Grid Stability:
 Balancing supply and demand to maintain grid stability despite the variability of
renewable energy generation.
 Requires advanced forecasting, grid flexibility, and energy storage solutions.
 Intermittency Management:
 Addressing the unpredictable nature of renewable energy generation through
forecasting and backup capacity.
Challenges

18. Time of Day Tariff - TOD

19. APPLICATIONS
Grid operation
Renewable
Energy
Integration
Infrastructure
Planning
Energy Trading

20. CONCLUSION
 Demand forecasting is essential for grid operations, energy trading, infrastructure
planning, renewable energy integration, demand-side management, and policy-
making.
 Key methods include statistical models, machine learning algorithms, and hybrid
approaches, each with advantages and limitations.
 Evaluation metrics assess forecast accuracy, bias, efficiency, and reliability.
 Further research and advancements in demand forecasting techniques are vital for
addressing emerging challenges, such as renewable energy integration, demand
variability, and grid modernization.

21. REFERENCE
 IEEE Transactions on Power Systems
 Electric Power Systems Research
 Electric Power Research Institute (EPRI)
 National Renewable Energy Laboratory (NREL)
 International Energy Agency (IEA)
 “Demand Forecasting for Electricity Distribution Systems" by Manfred Deutscher
 International Journal of Electrical Power & Energy Systems
 ourworldindata.org
 Pictures: unsplash.com