machine learning in energy – part two

This is the first of a two part series on the intersection of machine learning and the energy industry.

machine learning in energy – part one
Introduction, why it’s so exciting, challenges.

machine learning in energy – part two
Time series forecasting, energy disaggregation, reinforcement learning, Google data
centre optimization.


This post will detail three exciting applications of machine learning in energy:
– forecasting of electricity generation, consumption and price
– energy disaggregation
– reinforcement learning

We will also take a look at one of the most famous applications of machine learning in an energy system – Google’s work in their own data centers.

Forecasting of electricity generation, consumption and price

What’s the problem

In a modern electricity system time has a massive economic and environmental impact. The temporal variation in electricity generation and consumption can be significant. Periods of high consumption means generating electricity using expensive & inefficient peaking plants. In periods of low consumption electricity can be so abundant that the price becomes negative.

Electric grid stability requires a constant balance between generation and consumption. Understanding future balancing actions requires accurate forecasts by the system operator.

Our current energy transition is not making the forecasting problem any eaiser. We are moving away from dispatchable, centralized and large-scale generation towards intermittent, distributed and small scale generation.

Our current energy transition is moving us away from dispatchable, centralized and large-scale generation towards intermittent, distributed and small scale generation.

Historically the majority of generation was dispatchable and predictable – making forecasting easy. The only uncertainty was plant outages for unplanned maintenance.

Intermittent generation is by nature hard to forecast. Wind turbine power generation depends on forecasting wind speeds over vast areas. Solar power is more predictable but can still see variation as cloud cover changes.

As grid scale wind & solar penetration increase balancing the grid is more difficult. Higher levels of renewables can lead to more fossil fuel backup kept in reserve in case forecasts are wrong.

It’s not just forecasting of generation that has become more challenging.  The distributed and small scale of many wind & solar plants is also making consumption forecasting more difficult.

A solar panel sitting on a residential home is not directly metered – the system operator has no idea it is there. As this solar panel generates throughout the day it appears to the grid as reduced consumption.

Our current energy transition is a double whammy for grid balancing. Forecasting of both generation and consumption is becoming more challenging.

This has a big impact on electricity prices. In a wholesale electricity market price is set by the intersection of generation and consumption. Volatility and uncertainty on both sides spill over into more volatile electricity prices.

How machine learning will help

Many supervised machine learning models can be used for time series forecasting. Both regression and classification models are able to help understand the future.

Regression models can directly forecast electricity generation, consumption and price. Classification models can forecast the probability of a spike in electricity prices.

Well trained random forests, support vector machines and neural networks can all be used to solve these problems.

A key challenge is data. As renewables are weather driven forecasts of weather can be useful exogenous variables. It’s key that we only train models on data that will be available at the time of the forecast. This means that historical information about weather forecasts can be more useful than the actual weather data.

What’s the value to the world

Improving forecasts allows us to better balance the grid, reduce fossil fuels and increase renewables.

It’s not only the economic & environmental cost of keeping backup plant spinning. Incorrect forecasts can lead to fossil fuel generators paid to reduce output. This increases the cost to supply electricity to customers.

There are benefits for end consumers of electricity as well. Improved prediction can also allow flexible electricity consumption to respond to market signals.

More accurate forecasts that can look further ahead will allow more electricity consumers to be flexible. Using flexible assets to manage the grid will reduce our reliance on fossil fuels for grid balancing.

Sources and further reading

– Forecasting UK Imbalance Price using a Multilayer Perceptron Neural Network
Machine Learning in Energy (Fayadhoi Ibrahima)
7 reasons why utilities should be using machine learning
Germany enlists machine learning to boost renewables revolution
Weron (2014) Electricity price forecasting: A review of the state-of-the-art with a look into the future

Energy disaggregation

What’s the problem

Imagine if every time you went to the restaurant you only got the total bill. Understanding the line by line breakdown of where your money went is valuable. Energy disaggregation can help give customers this level of infomation about their utility bill.

Energy disaggregation estimates appliance level consumption using only total consumption.

In an ideal world we would have visibility of each individual consumer of energy. We would know when a TV is on or a pump is running in an industrial process. One solution would be to install metering on every consumer – a very expensive and complex process.

Energy disaggregation is a more elegant solution. A good energy disaggregation model can estimate appliance level consumption through a single aggregate meter.

How machine learning will help

Supervised machine learning is all about learning patterns in data. Many supervised machine learning algorithms can learn the patterns in the total consumption. Kelly & Knottenbelt (2015) used recurrent and convolutional neural networks to disaggregate residential energy consumptions.

A key challenge is data. Supervised learning requires labeled training data. Measurement and identification of sub-consumers forms training data for a supervised learner. Data is also required at a very high temporal frequency – ideally less than one second.

What’s the value to the world

Energy disaggregation has two benefits for electricity consumers. It can identify & verify savings opportunities. It can also increase customer engagement.

Imagine if you got an electricity bill that told you how much it cost you to run your dishwasher that month. The utility could help customers understand what they could have saved if they ran their dishwasher at different times.

This kind of feedback can be very effective in increasing customer engagement – a key challenge for utilities around the world.

Sources and further reading

7 reasons why utilities should be using machine learning
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
– Energy Disaggregation: The Holy Grail (Carrie Armel)
– Putting Energy Disaggregation Tech to the Test

Reinforcement learning

What’s the problem

Controlling energy systems is hard. Key variables such as price and energy consumption constantly change. Operators control systems with a large number of actions, with the optimal action changing throughout the day.

Our current energy transition is making this problem even harder. The transition is increasing volatility in key variables (such as electricity prices) and the number of actions to choose from.

Today deterministic sets of rules or abstract models are used to guide operation. Deterministic rules for operating any non-stationary system can’t guarantee optimality. Changes in key variables can turn a profitable operation to one that loses money.

Abstract models (such as linear programming) can account for changes in key variables. But abstract models often force the use of unrealistic models of energy systems. More importantly the performance of the model is limited by the skill and experience of the modeler.

How machine learning will help

Reinforcement learning gives a machine the ability to learn to take actions. The machine takes actions in an environment to optimize a reward signal. In the context of an energy system that reward signal could be energy cost, carbon or safety – whatever behavior we want to incentivize.

reinforcement_learning_in_energy

What is exciting about reinforcement learning is that we don’t need to build any domain knowledge into the model. A reinforcement learner learns from its own experience of the environment. This allows a reinforcement learner to see patterns that we can’t see – leading to superhuman levels of performance.

Another exciting thing about reinforcement learning is that you don’t need a data set. All you need is an environment (real or virtual) that the learner can interact with.

What’s the value to the world

Better control of our energy systems will allow us to reduce cost, reduce environmental impact and improve safety. Reinforcement learning allows us to do this at superhuman levels of performance.

Sources and further reading

– energy_py – reinforcement learning in energy systems
Minh et. al (2016) Human-level control through deep reinforcement learning
Reinforcement learning course by David Silver (Google DeepMind)

Alphabet/Google data centre optimization

One of the most famous applications of machine learning in an energy system is Google’s work in their own data centers.

In 2014 Google used supervised machine learning to predict the Power Usage Effectiveness (PUE) of data centres.

This supervised model did no control of its own. Operators used the predictive model to create a target PUE for the plant. The predictive model also allowed operators to simulate the impact of changes in key parameters on PUE.

In 2016 DeepMind published details of a how they applied machine learning to optimizing data centre efficiency. The technical details of this implementation are not as clear as the 2014 work. It is pretty clear that both supervised and reinforcement learning techniques were used.

The focus on the project again was on improving PUE. Deep neural networks predicted future PUE as well as future temperatures & pressures. The predictions of future temperature & pressures simulated the effect of recommended actions.

DeepMind claim a ’40 percent reduction in the amount of energy used for cooling’ which equates to a ’15 percent reduction in overall PUE overhead after accounting for electrical losses and other non-cooling inefficiencies’. Without seeing actual data it’s hard to know exactly what this means.

What I am able to understand is that this ‘produced the lowest PUE the site had ever seen’.

This is why as an energy engineer I’m so excited about machine learning. Google’s data centers were most likely well optimized before these projects. The fact that machine learning was able to improve PUE beyond what human operators had been able to achieve before is inspiring.

The potential level of savings across the rest of our energy systems is exciting to think about. The challenges & impact of our energy systems are massive – we need the intelligence of machine learning to help us solve these challenges.

Sources and further reading

Jim Gao (Google) – Machine Learning Applications for Data Center Optimization
– DeepMind AI Reduces Google Data Centre Cooling Bill by 40%

Thanks for reading!

 

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