Data analytics and modeling techniques to predict future energy consumption patterns

Energy forecasting is the process of predicting the future demand, supply, and price of energy sources such as electricity, fossil fuels, and renewable energy. Energy forecasting is important for various stakeholders in the power and energy industry, such as generators, utilities, consumers, traders, regulators, and policymakers. Through energy forecasting ACTA can help stakeholders optimize their operations, reduce costs, manage risks, plan investments, and implement policies.

Energy forecasting often utilizes time series data which is a type of data that consists of a sequence of values or events that are recorded over some period of time. Time series data can be used to analyze how variables change over time, or how they related to other variables over time.

Energy forecasting is also influenced by various factors, such as weather, economic activity, population growth, technology development, policy changes, and market conditions. These factors can introduce uncertainty and complexity into the forecasting process. Therefore, the use appropriate data sources, models, assumptions, and validation techniques is essential to ensure the accuracy and reliability of forecasts. ACTA’s research employs deep learning and soft computing methods for the development of robust energy forecasting tools.

The employed methods include:

• Fuzzy Cognitive Maps (FCMs)
• Adaptive Neuro Fuzzy Inference Systems (ANFIS)
• Fuzzy Logic
• Artificial Neural Networks (ANNs)
• Recurrent Neural Networks (RNNs) and their variants, such as Long Short-Term Memory (LSTM) networks
• Convolutional Neural Networks (CNNs)