Predictive Load Management Using
Advance Data Analytics
Where AI meets Demand Response!
BY TEAM NEXUS
2/19/2024
TEAM NEXUS | ENERGY HACKATHON
CONTENTS
INTRODUCTION
PROBLEM STATEMENT
MAIN ISSUES DEVELOPED BY THE PROBLEMS
APPLICABLE SOLUTIONS
WHAT, AND HOW TO DO?
ROADMAP & METHODOLOGY
ADVANTAGES
SWOT ANALYSIS
CONCLUSION
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INTRODUCTION
PREDICTIVE LOAD MANAGEMENT
Predictive load management is a sophisticated
process aimed at enhancing the efficiency and
reliability of power systems by forecasting future
energy demand and adjusting supply accordingly. It
integrates advanced technologies, data analytics,
and optimization strategies to ensure energy supply
meets demand in the most efficient way possible.
WAYS:
1. Data collection
2. Data Processing
3. Predictive modeling
4. Load forecasting
5. Optimization Strategies
6. Continuous Improvement
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INTRODUCTION
Demand Side Management:
❑ manage the demand for electricity in
the power grid.
❑ aims to optimize the use of electricity.
❑ reduce the peak demand, and improve
the overall efficiency of the power
system.
❑ help to reduce the need for new
power plants and other infrastructure.
❑ modify the energy consumption
pattern by
consumer.
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giving
incentive
to
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PROBLEM STATEMENT
Increasing
Energy Demand
The global increase
in energy demand is
driven by population
growth and industrial
expansion,
necessitating
innovative solutions
for sustainable
energy production
and consumption.
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Infrastructure
limit
Uneven Demand
Uneven energy
Limited infrastructures
demand across
for energy
transportation hinder regions, influenced by
economic disparities
efficient distribution,
and climatic
causing disparities in
variations, challenges
energy access and
the optimization of
urging investment in
energy production and
modern, resilient
necessitates flexible
energy networks.
energy systems.
TEAM NEXUS | ENERGY HACKATHON
Inaccurate\No
Historical Data
The lack of historical
data or inaccuracies
in existing datasets
complicates energy
forecasting and
planning,
underscoring the
need for improved
data collection and
analysis methods.
5
EXPLAINING THE PROBLEM STATEMENTS
Increasing Energy Demand
• High demand in winter
season and peak hours
• Importing Electricity from
neighboring country.
•
If unable to fulfill the
demand then load shedding
and blackouts.
source: Sustainability | Free Full-Text | Energy
Transition toward Cleaner Energy Resources in Nepal
(mdpi.com)
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EXPLAINING THE PROBLEM STATEMENTS
UNEVEN DEMAND
Energy consumption is
less in off peak hours
(Peak/average ratio is
less).
1600MW
650MW
Time(hours)
Consumers consumes
more electricity during
morning and evening
time of the day.
Source: High prices of imported electricity could affect power utility’s
finances (kathmandupost.com)
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EXPLAINING THE PROBLEM STATEMENTS
INFRASTRUCTURE LIMIT
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•
Lack of water reservoir
which leads to importing
electricity in peak
hours(Which is expensive)
•
Lack of two-way
communication between
utility and consumer.
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EXPLAINING THE PROBLEM STATEMENTS
INACCURATE\NO HISTORICAL DATA
Accurate and comprehensive
data on past electricity
consumption patterns is
crucial for understanding
current demand patterns and
forecasting future demand.
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ENERGY CONTRIBUTION SCENERIO OF NEPAL
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LOAD-DEMAND PORTFOLIO OF NEPAL
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ISSUES and their IMPACT
COMPARISON OF EXPORTED ENERGY TO INDIA IN FY 77/78, 78/79, 79/80
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ISSUES and their IMPACT
IMPORTED ENERGY FROM DIFFRERENT LINES IN FY 77/78, 78/79, 79/80
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SOLUTIONS
PREDICTIVE LOAD MANAGEMENT DEVICE(PLMD)
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Renewable
Integration
During Peak
Hours
User
Consumption
Data
Future
Demand
Prediction
Load
Scheduling
and Tariffs
Development
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SOLUTIONS
DYNAMIC TARIFF STRUCTURE
Flat tariff
Tariff with
fixed peak hours
No incentive
Shifting of peak
Real time tariff
structure
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LOAD CATEGORIZATION
Non- Deferrable Loads
Deferrable Loads
9
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METHODOLOGY
START
RUN HIGH
PRIORITY
APPLIANCE
YES
REAVAILABLE?
SCHEDULE MIDDLE AND LOW
ORDER APPLIANCE ACCORDING
TO RE AVAILABILITY
NO
STOP LOW AND
MIDDLE ORDER
APPLIANCE
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STOP
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ROADMAP
SCALING TO
LARGER AREAS
ANALYZING
EFFECTIVENESS OF
PLMD
DEPLOYMENT OF AI
EQUIPPED
PLMD IN SMALL
COMMUNITY
15
IDEA PRESENTATION TO
CONCERNED AUTHORITY
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ADVANTAGES
●
Helps to ensure the availability of enough capacity to meet tomorrow’s demand
● Automatic integration of Renewable Energy sources (if available)
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ADVANTAGES
Enhanced Grid Stability: By predicting demand, utilities can better manage
resources to ensure a stable supply and reduce the risk of outages.
Improved Energy Efficiency: Predictive analytics can identify patterns and
optimize energy distribution, leading to more efficient use of resources.
Cost Reduction: By optimizing energy production and distribution based on
predicted demand, utilities can reduce operational costs and potentially lower
costs for consumers.
Increased Use of Renewable Resources: Predictive models can help
integrate renewable energy sources into the grid more effectively by
forecasting availability and demand.
Customized Consumer Services: Utilities can offer dynamic pricing and
tailored services based on predictive insights into consumer behavior and
demand patterns.
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NEXUS || ENERGY
ENERGY HACKATHON
TEAM
HACKATHON
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SWOT ANALYSIS
WEAKNESS
STRENGTH
- Demand Prediction
- Peak PV Switching
- Incentive Earning
- Low Precision Sensors
- No Dynamic Tariffs
- Bit Expensive
OPPORTUNITIES
- Data Collection
- Smart Grid
- Net Metering
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THREATS
- Data Leaking
- PSU Disrupt
- Improper Handling
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VIDEO DEMONSTRATION
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HOW ENERGY DEMAND IS PREDICTED
Energy Consumption Pattern Is Different For Different People
But It Is Affected A Lot By Factor Such As :
●
●
●
●
Weather Condition
Holiday
Past Consumption Pattern
Festivals
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CONCLUSION
Demand Prediction
Deferrable Load Scheduling
Automatic Renewable Integration
Efficiency and cost saving
Adaptability to Future Challenges
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NEXUS||ENERGY
ENERGY HACKATHON
TEAM
NEXUS
HACKATHON
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DEMAND SIDE
MANAGEMENT IS
ACHIEVED
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THANK YOU!!!
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TEAM NEXUS | ENERGY HACKATHON
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3. SP Anjana and TS Angel. Intelligent demand side management for
residential users in a smart micro-grid, IEEE, 2017
4. Wan He. Load forecasting via deep neural networks. Procedia Computer
Science, 122:308–314, 2017
5. K Maharaja, P Pradeep Balaji, S Sangeetha, and M Elakkiya. Development of bidirectional net meter in grid connected Solar PV system
for domestic consumers. In 2016 International Conference on Energy
Efficient Technologies for Sustainability (ICEETS), pages 46–49. IEEE,
2016.
6. Asha Radhakrishnan and MP Selvan. Load scheduling for smart energy
management in residential buildings with renewable sources. In 2014
Eighteenth National Power Systems Conference (NPSC), pages 1–6.
IEEE, 2014
7. Judith Stute and Matthias K ̈uhnbach. Dynamic pricing and the flexible
consumer–investigating grid and financial implications, 2023
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