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Editor’s note: this article is by Mathieu Sinn, Research Manager and Francesco Dinuzzo, Research Scientist, members of the Exploratory Predictive Analytics, IBM Research-Ireland
We all plan. Predicting what we’ll need next week or next month. Businesses do the same. They forecast what kind, and how many of their goods and services will be needed by customers in the future. What if a machine could learn how, and forecast these demands? Maybe you, personally, wouldn’t want one to. But at our lab in Dublin, we’re developing machine learning algorithms for businesses, from retailers to energy and utility companies, to automate their demand forecasting.
Business planners rely on demand forecasting to find patterns from a multitude of data sources from internal and external factors. They use algorithmic models and predictive analytics to create a “demand forecast” which attempts to predict the amount of goods or services people, and businesses, will demand in the future. Retailers base in-stock management decisions like ordering and storage, as well as supply chain management, on demand forecasts. Energy utility companies use forecasting for scheduling operations, investment planning and price bidding. Now that we can integrate data from these disparate sources, our predictive analytics team is applying machine learning to improve demand forecasting accuracy and granularity.
Our team builds machines that learn from their mistakes and improve forecasting accuracy over time. Every time the machines apply the algorithm to compute a forecast, they are adjusting it to make it – and their predictions – better. Machine learning can automatically scale and adjust these predictive tasks. Instead of a domain expert building forecasting models manually, machine learning methods can combine large quantities of historical data and knowledge from the domain experts to learn relevant models automatically and with better predictive accuracy. Our analytics and systems are providing reliable forecasts for real-time operations that can seamlessly export the insights from data-at-rest into data-in-motion to support real-time operations. We see a great opportunity to use machine learning in demand forecasting and applying it across industries.
Through our predictive analytics research with energy demand forecasting and simulation we are seeing an increasing need for such data-driven and large scale forecasting solutions across industrial sectors which can also employ these same techniques.
Demand forecasting for smarter energy grids
We have been collaborating with VELCO (Vermont Electric Power Company) since last year to build the Vermont Weather Analytics Center. Its goal is to provide smarter grid resiliency and management, including renewable energy, using our energy demand forecasting systems and analytics capabilities alongside high resolution weather forecasting from the IBM Deep Thunder technology.
“Renewable energy production has a strong dependency on weather; likewise, energy demand also depends on weather. Therefore, high resolution, high accuracy forecasting will be a key enabler of the coming transition to clean energy,” said Chandu Visweswariah, IBM Fellow and Director of IBM’s Smarter Energy Research Institute.
“The project is built around a Vermont-specific version of Deep Thunder predictive weather model, coupled with a renewable energy forecasting model and an energy demand model. These models apply analytics to in-state and regional weather data to produce accurate weather, renewable energy and demand forecasts.”
We also recently published a paper with researchers from EDF (Electricite de France), one of the world’s largest energy providers, Massive-Scale Simulation of Electrical Load in Smart Grids using Generalized Additive “GAM” Models. In this paper, we describe how to simulate the energy load on a smart grid, including additional supply from renewable energy sources and demand from electric cars, to generate a more accurate energy demand forecast. Accurate load forecasts improve the efficiency of supply as they help utilities to reduce operating reserves, act more efficiently in the electricity markets, and provide more effective demand-response measures.
We concluded that by using real energy demand data, the right class of models and IBM tools like InfoSphere Streams, businesses can extract insightful models from data at rest, and deploy them directly to support real-time operations. And IBM SPSS Modeller can provide not only modelling, but visualization to help utilities quickly capture the changing trends in supply and demand, and rapidly deploy these insights to optimize the real time operations of the grid and production.
From these two energy and utilities projects, our team believes that we can replicate similar use cases across many different industries and businesses. We will share more of our findings this July at the 32nd International Conference on Machine Learning (ICML) in Lille, France.
Read more about our work with machine learning and demand forecasting on our research areas and applications page.