Abstract
The power consumption model can be represented in multiple dimensions, and it is proliferating to include structured and unstructured data. Dealing with such heterogeneous data and analyzing it in real-time is an ongoing challenge in the energy sector. Moreover, converting these data into useful information remains an open research area. This study focuses on modeling realistic and efficient power consumption data management in the heterogeneous environment for the Iraq energy sector and suggested a novel hybrid load forecasting model. The proposed system is named the Power Consumption Information and Analytics System (PIAS), which can perform various roles such as data acquisition from mechanical and smart meters, data federation, data management, data visualization, data analysis, and load forecasting. The proposed system has a four-tier framework (Data, Analytics, Application, and Presentation). Each layer is discussed in detail in this study to overcome the anticipated challenges. Furthermore, this study discusses the proposed system by applying two case studies. The first case study discusses power consumption data management, while the second introduces a novel hybrid load forecasting model using Fuzzy C-Means clustering, Auto Regressive Integrated Moving Average (ARIMA), and Gradient Boosted Tree Learner. The dataset used in this forecasting is based on a 1-year duration dated 1 January 2019 to 31 December 2019, on an hourly basis (365 * 24) for the Baghdad governorate. The results showed high accuracy in load forecasting with improved error rates (MAPE, MAE, and RMSE) achievements in comparison with other evaluated models such as standalone ARIMA and Gradient Boosted Trees methods.
Highlights
Modern technology in the energy sector is currently on the rise with the emergence of digital twin, smart grid, Internet of Things, big data analytics, machine learning, and artificial intelligence
It was found that support vector machine (SVM) regression gives 21% better accuracy in the power consumption forecasting problem, while in Argentina [36], a hybrid Auto Regressive Integrated Moving Average (ARIMA) and Regression Tree (RT) models have been used for short-term load forecasting (STLF), this study relied on an interval-valued time-series dataset
The results show that a combination of clustering and the ARIMA model has proved to increase the performance of the forecasting model more accurately than that using the ARIMA
Summary
Modern technology in the energy sector is currently on the rise with the emergence of digital twin, smart grid, Internet of Things, big data analytics, machine learning, and artificial intelligence. Energy’s big data has made an immense contribution in managing huge datasets containing information about energy consumption patterns, energy demand, etc., enabling the sector to bridge the demand and supply gap [4] Different data types, such as operational data, line data, transformer data, and load data can be collected and used together to enable the entire grid system to act intelligently. Big data analytics have been incorporated with smart grids to improve power distribution efficiency while optimizing energy consumption [5] It can be divided into three mains stages: data collection, communication, and pre-processing. Big data analytics can help detect faults through an automated system that is usually impossible in conventional systems It can facilitate real-time monitoring of all the consumers, obtaining accurate data related to power consumption patterns and eventually performing load profiling and forecasting [8].
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