Large-scale deployment of smart meters has made it possible to collect sufficient and high-resolution data residential electric demand profiles. Clustering analysis these profiles is important further analyze comment on electricity consumption patterns. Although many clustering techniques have been proposed in the literature over years, often noticed that different fit best for datasets. To ide...

Short-to-medium-term electric load forecasting is crucial for grid planning, transformation, and scheduling power supply departments. Various complex ever-changing factors such as weather, seasons, regional economic structures, enterprise production cycles exert uncontrollable effects on the load. While causal convolutional neural network can significantly enhance long-term sequence prediction,...

Load forecasting is an important problem in the operation and planning of electrical power generation. To minimize the operating cost, electric supplier will use forecasted load to control the number of running generator unit. Short-term load forecasting (STLF) is for hour to hour forecasting and important to daily maintaining of power plant. Most important factors in load forecasting includes ...

The electric arc furnaces are a very large power load, determining the negative effects on the power quality (harmonics currents, unbalanced load, and reactive power). For a maximum efficiency of the power consumption it is necessary to use an automat system for control the harmonics filters, the reactive power compensation installation and the electrodes position, in order to obtain a high val...

The buildings sector in Switzerland accounts for more than 40% of the country’s overall energy demand and CO2 emissions. The predicted future weather conditions, according to the Intergovernmental Panel on Climate Change (IPCC), will even reinforce this trend. Due to the foreseeable further electrification of heating and cooling demand in buildings their electric load demand will likely increas...

Providing reliable electrical service has always been a key focus for distribution planners. Traditional forecasting and planning approaches have historically used average load growth over wide areas, basic analysis of weather impacts, and little, if any, attention to changing economic, resource deployment, or network topology changes. Exactly how resources and loads operate, interact, and prov...

Multilayer perceptron network (MLP), FIR neural network and Elman neural network were compared in four different time series prediction tasks. Time series include load in an electric network series, fluctuations in a far-infrared laser series, numerically generated series and behaviour of sunspots series. FIR neural network was trained with temporal backpropagation learning algorithm. Results s...

The electric arc furnaces are a very large power load, determining the negative effects on the power quality (harmonics currents, unbalanced load, and reactive power). For a maximum efficiency of the power consumption it is necessary to use an automat system of electrodes position control, in order to obtain a high value of power factor and a maximum efficiency. In this paper is present an adap...

This paper develops medium term electric load forecasting using neural networks, based on historical series of electric load, economic and demographic variables. The neural network chosen for this work is the Time Lagged Feedforward Network (TLFN), which combines conventional network topology (multilayer perceptron) with good handling of time dependencies by means of gamma memory. This is a ver...