Big Data Analytics for Load Forecasting in Smart Grids: A Survey
Sana Mujeeb Nadeem Javaid Sakeena Javaid Asma Rafique Manzoor Ilahi
AbstractRecently big data analytics are gaining popularity in the energy management systems (EMS). The EMS are responsible for controlling, optimization and managing the energy market operations. Energy consumption forecasting plays a key role in EMS and helps in generation planning, management and energy conversation. A large amount of data is being collected by the smart meters on daily basis. Big data analytics can help in achieving insights for smart energy management. Several prediction methods are proposed for energy consumption forecasting. This study explores the state-of-the-art forecasting methods. The studied forecasting methods are classified into two major categories: (i) univariate (time series) forecasting models and (ii) multivariate forecasting models. The strengths and limitations of studied methods are discussed. Comparative anlysis of these methods is also done in this survey. Furthermore, the forecasting techniques are reviewed from the aspects of big data and conventional data. Based on this survey, the gaps in the existing research are identified and future directions are described.