ML Energy demand forecasting
Industry, companies, cities, households all consume energy. Whether opting for electricity, gas or household water – or, more likely, a combination of them – the need for energy is all around us. Heating and cooling our homes, lighting office buildings, driving cars and moving freight, and manufacturing the products we rely on in our daily lives are all functions that require energy. If projections are correct, we’re going to keep needing more.
Energy use can carry a hefty price tag—and not just in money. The cost to our environment, to national security, and to the prospect of future sustainability is sometimes hard to calculate.
Both consumers and producers can benefit greatly from accurate estimates of future consumption, not in the least because extreme volatility of wholesale prices force market parties to hedge against volume risk and price risk.
There are a wide variety of methods for electricity demand prediction ranging from those of the short term (minutes) to long term (weeks), while considering microscopic (individual consumer) to macroscopic (country-level) aggregation levels.
The renewable energy sector is where we see the most applications of ML and AI.
The renewable energy sources include sun, wind, rain, geothermal heat.
One of the most important factors to take into account with renewable energy is the fact that nature is unpredictable. This can make it hard to generate the necessary amount of energy required at any given time due to natural conditions.
There is considerable cost to electricity network operators associated with not being able to predict how much energy will be generated by, for example, a solar panel or a wind turbine. This cost is economic, but also operational, and even when only instantaneous, can lead to destabilization of the power grid.
Here is where machine learning and AI come into play. ML can contribute to making renewables a reliable power source. One major application of these tools is in developing forecast models, using deep neural networks.
Major advantages are:
- results are based on probabilistic distribution
- lightning fast performance
Our vision: Electricity has played an important role in innovation, progress, and communication and continues to become more and more important as engineers and scientists find more and more ways to use it. Therefore, it would be simple to assume that electricity will lead to much more innovation and technology as time goes on, including playing a very important role in transportation as other resources may become scarce.
We want to be part of that innovation and support the smart grids. We believe that the future of electrification is distributed and independent power generation and energy storage controlled by AI.
How we can help: We can build and train ML models and provide software infrastructure capable of processing thousands of variables
Deep neural network
Deep learning algorithms use different configurations of deep neural networks (DNN), architectures that most books and articles describe as a rough imitation of biological brains. A deep neural network is composed of several layers of artificial neurons stacked on top of each other. Each neuron is connected to several neurons in the next layer. As input data goes through the network, it gets processed by neurons in each layer and passed on to the next layer until it reaches the output layer. This is how deep learning models classify images, transform voice to text, predict stock prices, and perform many other complicated tasks. The smallest component of the DNN is the artificial neuron. Every neuron is a function for a linear transformation, a very simple operation that you’ve learned in high school. In a nutshell, a linear transformation modifies an input number by multiplying it by one or more weights and adding a bias value. The weights and bias are also called the parameters of the neuron. Read more