Decentralized peak power control based on short-term forecast using modified Kalman filter

This paper proposes a method of short term load forecasting with limited data, applicable even at 11 kV substation levels where total power demand is relatively low and somewhat random and weather data are usually not available as in most developing countries. Kalman filtering technique has been modified and used to forecast daily and hourly load. Planning generation and interstate energy exchange schedule at load dispatch centre and decentralized Demand Side Management at substation level are intended to be carried out with the help of this short term load forecasting technique especially to achieve peak power control without enforcing load-shedding

Short-term forecasting for decentralised load management

For decentralised load management, short-term forecasts of the load on small and medium-sized distribution substations are essential. A suitable prediction method is proposed which exploits the intrinsic pattern in the load recorded hourly at local supply centres. The method uses a fading-memory Kalman filter/predictor algorithm. The results of a study carried out with real-world data to estimate the extent to which local load is amenable to prediction is also presented

Short-term load forecasting for demand side management

A method for short-term load forecasting which would help demand side management is presented. This is particularly suitable for developing countries where the total load is not large, especially at substation levels, and the data available are grossly inadequate. It is based on the Kalman filtering algorithm with the incorporation of a 'fading memory'. A two-stage forecast is carried out, where the mean is first predicted and a correction is then incorporated in real time using an error feedback from the previous hours. This method has been used to predict the local load at 11kV and also the bulk load at 220kV. The results and the prediction errors are presented

Short-term electricity demand forecasting based on multiple LSTMs

In recent years, the problem of unbalanced demand and supply in electricity power industry has seriously affected the development of smart grid, especially in the capacity planning, power dispatching and electric power system control. Electricity demand forecasting, as a key solution to the problem, has been widely studied. However, electricity demand is influenced by many factors and nonlinear dependencies, which makes it difficult to forecast accurately. On the other hand, deep neural network technologies are developing rapidly and have been tried in time series forecasting problems. Hence, this paper proposes a novel deep learning model, which is based on the multiple Long Short-Term Memory (LSTM) neural networks to solve the problem of short-term electricity demand forecasting. Compared with autoregressive integrated moving average model (ARIMA) and back propagation neural network (BPNN), our model demonstrates competitive forecast accuracy, which proves that our model is promising for electricity demand forecasting