Lightweight Shortterm Photovoltaic
Power Prediction forMobile Edge
ComputingAlbert Y. Zomaya
Chair Professor and Director
Centre for Distributed & High Performance Computing
School of Computer Science
The University of Sydney
Sydney, NSW, Australia
https://albertzomaya.github.io/My-WebPage/

The University of Sydney

Page 1–––The University of Sydney

11 Researchers
20 Affiliates (Australia, Asia, Europe, South America,USA)
40+ graduate students

Page 2Our Research
Research article abstracts of last 5 years…..

The University of Sydney

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Agenda Background & Motivation
 Methodology & Implementation
 Experiment & Result Analysis Summary Future WorkThe University of Sydney

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Background (Massive Investments inClouds)

1984 – John
Gage, Sun
Microsystems“The network isthe computer”

The University of Sydney

1993 – Eric
Schmidt, Google“When thenetwork becomes
as fast as

2008 – David
Patterson, U. C.
Berkeley“The data centeris the computer”

the processor, thecomputer hollows
out and spreads
across thenetwork.”

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Edge Computing Milieu
Data Analytics

Network Edge

Transmission Latency

High

Cloud

GlobalIntelligence

Cloud Data Centres
MediumLocal
Intelligence

Edge Computing

Edge Computing

Edge Computing

Safe???

Low

Edge Computing

Safe6Privacy
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Background (Edge Computing, FogComputing, …)• local processing power close to the source of data
• Traditional edge processing power is given to the IoT device itself• While in fog computing, computing nodes (e.g., Dockers and VMs) are placed very close the source of data.
• The ‘edge computing’ paradigm depends on programmability of IoT devices to directly communicate
with each other and run user defined codes.
• Unfortunately, standard APIs that provide such functionality are not fully adopted bycurrent IoT sensors/actuators, etc.

The University of Sydney

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Background (IoT)© IDG
IoT: the term coined in1999 by Kevin Ashton, co-founder of Auto-ID
Center at MIT. IoT as enabler of a world where physical objects aretagged and uniquely identified by RFID transponders.
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Rapid Data Generation– Internet of Things••

Growing at an ever-increasingrate
By 2025 ▬ over 70 billion
Internet of Things devices
connected– Big Data•
•Doubles every 2 years
By 2020 ▬ 44Zettabytes or 44
trillion gigabytes– A big beneficiary of Edge
Computing is IoT.
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Source: https://www.statista.com/statistics/471264/iot-number-ofconnected-devices-worldwide/

Page 9Motivation

The University of Sydney[Optimal Application Deployment in Resource Constrained Distributed Edges,IEEE Transactions on Mobile Computing, 2020]

Deployments of edge computing can lead to high energy consumption

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[Mobility-Aware Service Selection in Mobile Edge Computing Systems, ICWS, 2019]

More Opportunities

Home Router
Networking Devices

Personal
Cloud Stores
e.g. Dropbox

Personal Devices

Trusted Userse.g. family

TrustworthinessResources

Personal
Cloud

Trusted Edge
Cloud

TraditionalEdgeCloud

Traditional
Cloud

Spatio-temporal dependent characteristics of networking infrastructure

Elastic Computing Platforms for Efficient and Trustworthy Mobile Data
Processing and Delivery

The University of Sydney

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[Building an Online Defect Detection System for Large-scale Photovoltaic Plants, ACM
BuildSys, 2019]

ExampleThe University of Sydney

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Potential Problems• Hard to utilize solar energy as stable source as
opposed to utility grids.
• The uncertainty of load demand leads todifficulty in resource allocation.
• Degradation in system performance due to lackproper resource management.

The University of Sydney[Request Dispatching for Minimizing Service Response Time in Edge Cloud Systems,
ICCCN, 2018. (Invited Paper)]Page 13

A Complex Problem
Design a lightweighttraining framework

Adaptive Resource Management for
Sustainable Edge Computing Systems

Renewable Energy
Prediction

Realise interpretable
analysis for theprediction models

Energy Demand
Estimation

Sustainable Edge
Computing System
System Building and
Performance Evaluationof the Design

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Establish lightweight
prediction models

Achieve energy
disaggregation of edge
devices
Design an energy
demand estimator
Develop an online
energy management
framework
Develop a dynamic
multi-objective resourcescheduler[Optimal Application Deployment in Resource Constrained Distributed Edges,
IEEE Transactions on Mobile Computing, 2020]

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Challenges of PV Power Prediction
 Lack of a cost-effective model for PV output prediction, which not only can
improve accuracy but lower the computation overhead.
 Few predictive models take temporal patterns of solar energy generation intoconsideration. (A temporal pattern is defined as a segment of signals that recurs
frequently in the whole temporal signal sequence).
 Models with high accuracy mostly require high computation and storage
resources in runtime, which will t edge computing systems with limited
resource.
 The existing lightweight models cannot provide accurate prediction asexpected.
[Interpretable Machine Learning In Sustainable Edge Computing: A Case Study of Short-Term
Photovoltaic Power Output Prediction. ICASSP, 2020. (Invited paper)]The University of Sydney

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Current State of the Art for Solar PowerPrediction• Classical Statistical Algorithms
I. Numerical weather prediction (NWP)
II. Auto-Regressive and Moving Average Model (ARMA)• Machine Learning Algorithms
I. Extreme learning machine (ELM)
II. Support vector regression (SVR)
III. General regression neural network (GRNN)
IV. Recurrent Neural Network (RNN)

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Model Training and PredictionTemporal Patterns Aggregation



Weather ClusteringModel Establishment

Interpretability Evaluation

Framework of clustering-based prediction model

The University of Sydney[Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence.
IEEE Internet of Things Journal, 2020]

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Methodology & Implementation
Meteorological Features

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The ghi is highly influenced by the weather condition. (e.g., sunny, partially cloudy, and Page 18cloudy)

Methodology & Implementation
Temporal Patterns Aggregation

The iterative strategy for temporal pattern aggregation

Feature Vector for Clustering{,...,,: aggregated feature vectors at time t for weather
clustering: meteorological patterns at time t: aggregated feature vectors at time t for model training
and PV output prediction: irradiance factors at time t, including ghi, dhi and dni: actual PV power output at time t}

Feature Vector for Prediction{,The University of Sydney,...,,, ,}Page 19Methodology & Implementation
Temporal Patterns Aggregation

RMSE(root mean square error)

Accuracy of four prediction models with different sizes of time step(which is non-linear)
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Methodology & Implementation
Weather Clustering• Motivation
I. Reduces spikes and falls in the PV output curves.
II. Builds up a clustering-based model to reduce prediction errors.
III. Copes with the potential issue of data loss.
• ApproachI. Tree-structured Self-organizing Map (TS-SOM)
II. Divides the data set into multiple groups and each node of thetree is designed as a traditional SOM neural network.
III. Enables detailed weather clustering gradually from the root tothe leaf.
IV. Fit for clustering of the hierarchical data.
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Methodology & Implementation
TS-SOM for Weather Clustering

Set threshold for the tree tostop the TS-SOM recursion

Enable detailed weatherclustering recursivelyUpdate global informationof the tree (tree attributes)
after each split

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The Framework of Tree-based SOM

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Methodology & Implementation
Power Output Prediction Model•

Tree-based ensemble regression
method: LightGBM•

Ensemble a set of weak learners
generated at different training time steps,
mostly using Classiﬁcation andRegression Tree (CART).

•Sums up their results as the ﬁnal
prediction output iteratively.

•

Establish a prediction model for eachweather cluster.

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Methodology & Implementation
Power Output Prediction Model
Novel techniques of LightGBM•

Histogram-based split algorithm•

Gradient-based one-side sampling (GOSS)•

Exclusive feature bundling

Advantages of LightGBM•

Faster training speed and higher efficiency•

Lower memory usage•

Better accuracy•

Compatibility with Large Datasets•

Parallel learning supported

The University of Sydney

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Interpretability Analysis for Prediction
ModelsDefinition
Interpretable machine learning is a technique used to give machine learningmodels the ability to explain or to present their behaviors in understandable
terms to humans.
Background & Motivation• Machine-learning models have demonstrated great success in learningcomplex patterns and making predictions about unobserved data.
• However, complex models lack transparency behind their behaviors, whichleaves users with little understanding of how particular decisions are madeby these models.
• The concerns about the black-box nature of complex models have
hampered their further applications.
• Interpretable machine learning would be an effective tool to mitigate these
problems and it has recently received considerable attentions.

The University of Sydney[Edge-Computing-Enabled Unmanned Module Defect Detection and Diagnosis System forLarge-Scale Photovoltaic Plants. IEEE Internet of Things Journal, 2020]
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Interpretability Analysis for Prediction
ModelsTraditional Data Science Life Cycle

Raw data

Feature EngineeringFeatures

ML ModelOutputInterpretation In Data science Life Cycle

Problem, Data &Audience

Predictive
accuracy

Descriptive
accuracy
Model Training

Interpretability
Analysis

IterateThe University of Sydney[Realising Edge Analytics for Early Prediction of Readmission: A Case Study, IC2E, 2020 Page 26(Invited paper)]Interpretability Analysis for Ensemble
ModelsFeature importance is a simple yet effective explanatory measure toindicate statistical contribution of each feature to the underlying model.

These traditional attribution
methods violate theconsistency principle offeature attribution

Traditional Feature
Attribution Methods:

Weight: Count the
number of times afeature used to performthe node partitions
across the forest.

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Gain: Measure theaverage training loss
reduction as the gain
when using a featurefor node partition.

Cover: Calculate theaverage coverage ofnode splits for a certainfeature.

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Interpretability Analysis for Ensemble
ModelsSHapley Additive Explanation (SHAP)
SHAP is an additive feature attribution method that explains a model’s output as a sum ofreal values attributed to each input feature.

Advantages of SHAP•

SHAP averages over all possible orderings of the features, rather than just theordering speciﬁed by their position in the tree, which is consistent.

•SHAP contains the information of both global explanation and localized explanation forindividual prediction.

The University of Sydney

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Experiments & Results Analysis
Experimental Setup• Powerful Desktop• Raspberry Cluster

 i7-7700 (6c/12t) CPU
 32GB DDR3 Memory
 2T SSD 7200rpm Hard Disk Ubuntu 16.04

 Raspberry Pi 3B
 Quad-core CPU
 1GB RAM
 64-bit Ubuntu Mate
 Dask Framework• Software PackagePython 3.7, Scikit-learn 0.21.3 and Tensorflow 1.13.2.

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Experiments & Results Analysis
Evaluation Metrics
 Mean Absolute Error (MAE) Training Time Cost

 Root Mean Squared Error (RMSE)

 Prediction Time Cost Coefficient of determination (denoted as “R2 ”)
Proportion of the variance in the dependent
variable that is predictable from the independentvariable.

The University of Sydney

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Experiments & Results Analysis
Prediction Performance Evaluation

Two ensemble methods (LightGBM and XGBoost) dominate on all metrics and provide better performance on
the test data set with higher accuracy and a lower error rate compared to the others. Besides, with more than
0.97 of R2, these two models indicate that they can also provide better generalization performance and fit todiverse weather conditions.
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Page 31Experiments & Results Analysis
Prediction Performance Evaluation

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Experiments & Results Analysis

PV power output prediction for a typical day
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Experiments & Results Analysis
Cost on Powerful Desktop

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Cost on Raspberry Pi Cluster

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Experiments & Results Analysis
Interpretability EvaluationThe feature importance of ”ghi” isthe highest, which means it makes
the most contribution to theprediction.

It is noteworthy that ”ghi” makes
the most impacts on mostpredictions.

SHAP indeed guarantees theconsistency in prediction modelby comparing results of Gain
and Weight.
The ghi is highly influenced by the weather condition. (e.g., sunny, partially cloudy, andcloudy)
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Experiments & Results Analysis
Interpretability Evaluation

Results of Gain

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Results of Weight

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Summary

Our proposed clustering-basedmodel surely provides high-level
performance on tackling theproblem of solar power
prediction and lowers theresource overhead whencompared to other widely used
regression algorithms.

The feature ”ghi” makes the mostcontribution on PV output
prediction, and the characteristics
of solar power generation are
also reected in the orderings offeature importance.

[A Lightweight Short-term Photovoltaic Power Prediction for Edge Computing, IEEE Trans on
Green Communications and Networking, 2020]
The University of Sydney

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Future Work
Enhance generalization ability of the proposed PV
output prediction

Overcome multiple challenges related to resource
management on edge devices

Develop a dynamic multi-objective resourcescheduler

The University of Sydney

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Finally….
– Edge Computing is a rapidly evolving technology with many
opportunities, and many more challenges.
– Performance predictability is a major concern in current Edge
systems where heterogeneous resources are to be allocated.
– Realistic performance metrics are needed at the applicationlevel to collectively and truly represent performance variances
occurring across all system-level components.
– New optimisation models that integrate a variety of resources
with strict real-time/capability constraints.
– Trade-off frontiers between an Edge system performance andassociated costs. This is necessary to inform resource allocation
across a platform as well as well to cap expenditure.

The University of Sydney[Federated Learning over Wireless Networks: Convergence Analysis and Resource
Allocation, IEEE/ACM Transactions on Networking, 2021]

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Things in the Pipeline––––––Online machine learning for real-time energy disaggregation for electricity distribution feeders
Low latency smart meter (computation and communication) – implemented and tailored toend users
Residential demand response/thermal loss minimization studies in Eco capsules
Smart water systems management
Mobile blockchain + edge computing on e-health applications
Multiple data sources (visible/infrared/electric sensing) integration/analytic for the pitfalls
detection of PV panels

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Thank YouQuestions?

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