Machine Learning and Energy Consumptionare two very important topics these days. One might call them trends, and they are more than just buzzwords.
In this article, we explain why these two concepts are intrinsically linked andhow new technologies help improve sustainability and reduce energy consumption.
The amount of energy we use keeps growing, and with that comes increased greenhouse gas emissions. At the same time, Machine Learning is continually improving, with better techniques and algorithms. When applied to the energy sector, the future might suddenly be more efficient.
This is why companies, individuals and organisations are putting significant effort into finding creative ways of applyingMachine Learning and Artificial Intelligence在能源行业,特别是在找工作ing ways to accurateforecasts of energy consumption, andthe performance of renewable sources.
What is Machine Learning?
的噪音,所以我们都在same page, it is worth introducingmachine learningto those who either don’t know what it is or are still not completely sure.
As the name suggests,machine learningis a way of getting a softwareprogram to learn how to improve its performance on a task by itselfthrough experience.
These programs are made using algorithms and statistical models that have been developed and improved through research over the years. In order for them to work, they require good data, which they process, and learn from.
The worddatais key here because just as we humans learn from data (things we listen to, read or experiment with),programs using machine learning algorithmsuse data provided by developers and engineers to train the algorithms.
What kind of data is needed by these algorithms?
We can basically use any relevant type of data. The more, the merrier, to get better results.
In the energy industry, thisdatasetis passed to a machine learning algorithm that is literally “trained”, making it possible for us to predict or accurately estimate the future energy consumption of devices or loads.
An example of a tool that uses Machine Learning and AI for effective energy consumption forecasting is theEnergy Management Platform, Spacewell Energy (Dexma). This EMS (Energy Management Software) offers an all-in-one solution, based on artificial intelligence and advanced analysis. In case you’re interested, here’s a link to thedemo.
How do Machine Learning and AI forecast Energy Consumption
When it comes toconsuming energy, it is often the case that we maintain our habits and behaviours until something unexpected triggers our awareness. For instance,skyrocketing electricity bills, orlower-than-expected performancefrom renewable energy sources.
Here is wheremachine learning and artificial intelligence come into play. One major application of these tools is in developing ways to forecast energy consumption, usingdeep neural networksandregression analysis.
By processing energy consumption data, for example, thehistoricaldata of energy consumedby a building from 1996 to 2015, it is possible for amodelto reveal trends and patterns, but also, topredict future energy consumptionpatterns.
The 3 major benefits of Energy Consumption Forecasting:
- Economic:Companies and individuals can translate energy into cost, and therefore estimate their energy bill, and make decisions based on this.
- Practical:By not only knowing how much energy we will consume but understanding how and why we will, we can change our habits without affecting our productivity or the quality of our lives.
- Technical:Better managing energy data unlocks new possibilities in the collection and analysis of this data, as well asin generating more accurate predictions…
7 Methods you can use for an Effective Energy Consumption Forecasting
1. Time seriesare among the most used forenergy consumptionor productionprediction.
For instance, when it comes to forecasting the behaviour of wind: such as speed and direction. It is fundamental to input the data at regular intervals of time so that the model can learn by the evolution of this data over time.
Time series forecastinguses techniques such asautoregressive,moving average,autoregressive moving average, and vector autoregressive, among others; which make it possible to predict the output of energy sources based on past observation.
2. Artificial Neural Networks (ANNs),which comprise what is called deep learning, an advanced form of machine learning inspired by the way the brain of animals works.
Deep learning is highly valuablein the energy industryas the algorithms are suited for large datasets. In the case of historicalenergy consumption and generation, data sets tend to be massive and require the right techniques so they can beprocessed and analysedefficiently.
3. Ensemble Methods for Enhanced Accuracy
Ensemble methods, a collection of ML techniques that combine the predictions of multiple models to improve accuracy and robustness, offer a powerful approach to energy consumption forecasting. By integrating the outputs of various algorithms, such as decision trees, support vector machines, and neural networks, ensemble methods can mitigate individual model biases and errors. This results in more reliable and precise predictions, especially when dealing with complex and dynamic energy consumption patterns. In addition, ensemble methods enable theincorporation of multiple types of data sources, including historical consumption data, weather conditions, economic indicators, and social factors, leading to more holistic and comprehensive forecasts.
4. Incorporating External Factors
While historical energy consumption data forms the core of forecasting models, the inclusion of external factors can significantly enhance their predictive capabilities. External factors encompass a wide range of variables, such as public holidays, industrial activities, events, and policy changes, which can influence energy consumption patterns. Integrating these factors into ML algorithms enables the models to capture the intricate relationships between energy usage and external dynamics, resulting in more accurate and adaptable forecasts.This dynamic approach is particularly crucial in sectors with varying consumption behaviours and evolving external influences.
5. Transfer Learning for Adaptability
Transfer learning, a concept within ML where knowledge gained from one domain is applied to another related domain, offers an innovative approach to energy consumption forecasting. By leveraging pre-trained models on similar energy consumption datasets, transfer learning can significantly reduce the time and resources required to train new forecasting models from scratch.This is especially valuable in scenarios where there is limited data available or when transitioning from one region to another with distinct consumption patterns. The ability to fine-tune pre-trained models to specific contexts enhances the adaptability of energy consumption forecasting algorithms, ensuring accurate predictions across diverse settings.
6. Online Learning for Real-time Adjustments
The dynamic nature of energy consumption necessitates real-time adjustments in forecasting models.Online learning, a technique where models are updated continuously as new data becomes available, addresses this need by enabling models to adapt to changing consumption patterns on the fly. This approach is particularly advantageous when dealing withrapidly evolving situations, such as sudden changes in demand or the integration of new renewable energy sources. By incorporating the most recent data, online learning ensures that forecasting models remain up-to-date and capable of providing accurate predictions in rapidly changing environments.
7. Explainable AI for Stakeholder Confidence
As ML and AI techniques become increasingly integrated into energy consumption forecasting, the need for transparency and interpretability becomes paramount. Explainable AI methods, which provide insights into how models arrive at specific predictions,enhance stakeholder confidence and foster trust in the forecasting process. Interpretable models allow energy managers, policymakers, and other stakeholders to understand the underlying factors driving forecasts and make informed decisions based on the generated insights. This transparency is essential for facilitating the adoption of AI-powered forecasting tools across diverse industries and sectors.
Incorporating these advanced methods and strategies into energy consumption forecasting not only enhances the accuracy of predictions but also broadens the application of ML and AI techniques in the energy sector. By continuously improving the quality of forecasts and enabling adaptable models, the integration of advanced technologies offers the potential to revolutionise energy management practices and contribute to a more sustainable and efficient energy future.
How is it possible to predict Renewable Energy Consumption?
Apart from the practical and economic aspects of consuming energy, and thepositive impact of predicting energy consumptionfor consumers;the reduction of the impact of energy production and consumption on the environmentis another big opportunity unlocked by machine learning.
When we talk aboutrenewable energy sources, we talk about the sun, wind, rain, geothermal heat, etc. These are used toproduce renewable energy, whose impact on the environment is very low.
Theseclean, environmentally friendly and renewable ways of generating energy are the “holy grail”for many countries, regions, individuals and organisations; however, there are many difficulties in reaching ambitious targets and goals.
One of the most important factors to take into account with renewable energy is the fact thatnature is unpredictable. As unsurprising as it might sound, this is key, as this can make it hard to generate the necessary amount of energy required at any given time due to natural conditions.
电力网络有相当大的代价operators associated with not being able topredict how much energy will be generated by, for example, asolar panelor a wind turbine. This cost is economic, but also operational, and even when only instantaneous, can lead to the destabilisation of the power grid.
Collecting data onbetway live and other climate conditions can feed a model toknow in advance whether enough energy will be generated, and transition to non-renewable energy with enough time if necessary.
There is a combination of data required for this type of prediction to be performed efficiently, and new initiatives and projects are continuously being developed for this. You can check ourR&D projectsat any time.
You can also read more about theuse of AI in Energy Management in this article.
