Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Electrical load forecasting plays a vital role in order to achieve the concept of next
generation power system such as smart grid, efficient energy management and better power …

This paper presents ensemble models for forecasting big data time series. An ensemble
composed of three methods (decision tree, gradient boosted trees and random forest) is …

Energy management systems are designed to monitor, optimize, and control the smart grid
energy market. Demand-side management, considered as an essential part of the energy …

Building energy consumption prediction plays an important role in improving the energy
utilization rate through helping building managers to make better decisions. However, as a …

Traditionally, short-term electricity price forecasting has been essential for utilities and
generation companies. However, the deregulation of electricity markets created a …

We investigate the application of convolutional neural networks for energy time series
forecasting. In particular, we consider predicting the photovoltaic solar power and electricity …

This paper addresses the problem of energy consumption prediction using neural networks
over a set of public buildings. Since energy consumption in the public sector comprises a …

The electric load forecasting is extremely important for energy demand management,
stability and security of power systems. A sufficiently accurate, robust and fast short-term …

In this paper, the historical power load data from the National Electricity Market (Australia) is
used to analyze the characteristics and regulations of electricity (the average value of every …