Forecasting electricity demand requires accurate and sustainable data acquisition systems which rely on smart grid systems. To predict the expected by grid, many meters are required to collect sufficient data. However, problem is multi-dimensional simple power aggregation techniques may fail capture relational similarities between various types of users. Therefore, forecasting energy plays a ke...

Short-term Load Forecast (STLF) is a load forecasting that very important to study because it determines the operating pattern of electrical system. Forecasting errors, both positive and negative, result in considerable losses costs increase ultimately lead waste. STLF research Indonesia, especially State Electricity Company (PLN Sulselrabar), has yet be widely used. Methods mainly used are man...

Accurate power load forecasting has a significant effect on smart grid by ensuring effective supply and dispatching of power. However, electric data generally possesses the characteristics nonlinearity, periodicity, seasonality. For complex systems, presence redundant information potentially reduces real pattern extraction for forecasting. Bearing in mind these issues, we propose an model which...

Short-term load forecasting is a significant component of safe and stable operations economical reliable dispatching power grids. Precise can help to formulate reasonable effective coordination plans implementation strategies. Inspired by the spiking mechanism neurons, nonlinear neural P (NSNP) system, parallel computing model, was proposed. On basis SNP systems, this study exploits fresh short...

Although the accuracy of load forecasting has been studied by many works, actual deployability a model is rarely considered. In this work, we consider from four aspects: 1) prediction performance model; 2) robustness 3) dependence on external data; and 4) storage size model. From these aspects, propose multiple wavelet convolutional neural network (MWCNN) for forecasting. On two public data set...

A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and Internet things (IoT), where digitalization at core energy sector transformation. However, grids require managers become more concerned about reliability security systems. Therefore, planners use various methods technologies to support sustainable expansion systems, such as ele...

Short-term power load forecasting is critical for ensuring system stability. A new algorithm that combines CNN, GRU, and an attention mechanism with the Sparrow to optimize variational mode decomposition (PSVMD–CGA) proposed address problem of effect random fluctuations on accuracy short-term forecasting. To avoid manual selection VMD parameters, adopted by decomposing data into multiple subseq...

With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these as they provide detailed load data. However, using smart meter data forecasting is challenging due to privacy requirements. This paper investigates how requirements be addressed through a combinat...