When Weather Matters: IoT-Based Electrical Load Forecasting for Smart Grid

Abstract

Electrical load forecasting is still a challenging open problem due to the complex and variable influences, e.g. weather and time. Although, with the recent development of Internet of Things (IoT) and smart meter technology, people have obtained the ability to record relevant information on a large scale, traditional methods struggle in analyzing such complicated relationships for their limited abilities in handling non-linear data. In the paper, we introduce an IoT-based deep learning system to automatically extract features from the captured data, and ultimately, give an accurate estimation of future load value. One significant advantage of our method is the specially designed two-step forecasting scheme, which significantly improves the forecasting precision. Also, the proposed method is able to quantitatively analyze the influences of some major factors, which is of great guiding significance to select attribute combination and deploy on-board sensors for smart grids with vast area, variable climates and social conventions. Simulations demonstrate that our method outperforms some existing approaches, and can be well applied in various situations. Index Terms-Smart grid, Internet of things (IoT), load forecasting, metering infrastructure, big data.

Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

“…For the SVR and EMD-SVR models, the kernel function and free parameters needed to be selected when establishing the SVR model. In this paper, the Gauss Radial Basis Function was selected as the kernel function of SVR, and the penalty parameter c and the kernel parameter g were selected by the grid search method, where the range of c is [10 −4 , 10 4 ], and the range of g is [2 −4 , 2 4 ]. For RF and EMD-RF models, the maximum number of decision trees n_estimators was optimized by grid search when establishing the RF model, and its range is set to [10,100].…”

“…Short-term electricity load forecasting is one of the main tasks for the grid dispatching operation department, and its accuracy is closely related to the formulation of dispatching plans and the proposal of transmission schemes. However, many factors affect the change of short-term load, which causes the load series to be highly non-linear and non-stationary, thus high-precision prediction of short-term load is a challenging task [4].…”

Review of the Complexity of Managing Big Data of the Internet of Things

“…The wireless positioning method has been improved with the usage of the Stacked Denoising Autoencoder and that also improves the performance by creating reliable features from a large set of noisy samples [92]. The prediction of home electricity power consumption has been analysed with a deep learning system that automatically extracts features from the captured data and optimises the electricity supply of the smart grid [93].…”

A Review of the Internet of Floods: Near Real-Time Detection of a Flood Event and Its Impact

“…IoT could help remedy inadequate emergency preparedness and planning, which in turn could reduce the magnitude of the physical and human damage in such events [24]. With IoT, decision-makers can get a heightened awareness of real-time events, such as floods [25], from a large number of sensors that report on environmental conditions (including soil moisture [26], ocean currents, or weather [27,28]) and the situation in and from infrastructures (e.g., roads [29] and buildings [30]). As Mazhar Rathore M. stated, "In a smart city, we perform real-time decision making on real-time data" [31].…”