Abstract
Advancements in different types of electrical meters and computing technologies aiding the data collection and sensing of various parameters of the electrical power system has been made possible with the availability of vast amount of electrical data. With the help of such technology and data, statistical prediction of load can be made smarter and more accurate. This can help stop excessive electricity production. With the help of deep learning techniques such as a long-short-term neural network (LSTM), it is possible to build time-series models that map non-linear parameters that can be used for precise memory sequences. An increase in recognition is witnessed in the field of forecasting with a short-term demand. In the field of power system control, it is now considered important. When proper pre-data is available, precision results can be high. Here, we are employing long short term neural network to forecast the load of a sample household.
Highlights
For effective load forecasting of a concise region, previous data is necessary to understand the historical pattern. This estimation is done by employing time series, longitudinal data, or cross-sectional methods
There has been very few prediction models built for electrical load forecasting
recurrent neural network (RNN) usually take a single or many input vectors which leads to the output(in the form of vectors).The factor that affects the output is the weights, because a “hidden” state vector is formed that is formed based on the prior input(s) or output(s)
Summary
For effective load forecasting of a concise region, previous data is necessary to understand the historical pattern. To employ methods that require prior information to be handled, we use neural networks that can use historical data as a source for formulating better algorithms. Most of the load forecasting has been built to predict a short-term output ranging around 2-3 week using Neurofuzzy logic, feed forward neural networks and basic support vector machines. Majority of the mentioned works employ a yearly resolution of Maximum energy demand (MED) or Total Energy Utilization (TEU) for prediction of load ranging up to a decade. In this paper a long short-term memory neural network model is implemented for forecasting power demand of a household for a week. To train this model, real time power statistics are provided by the archives of UC Irvine. An arduous and a taxing process for a low-end machine, it is gratifying
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