Covering a wide area by large number of WiFi networks is anticipated to become very popular with Internet-of-things (IoT) and initiatives such as smart cities. Such network configuration normally realized through deploying access points (APs) overlapped coverage. However, the imbalanced traffic load distribution among different APs affects energy consumption device if it associated loaded AP. T...

These days, limited energy resources, and also the cost of investment in new and renewable energy is very costly and uneconomical. In the industrial sector, especially energy-intensive industries such as steel, finding appropriate solutions to update the technology, production and reduce energy consumption in this sector is very important. The main aim of this study is the identification an...

These days, limited energy resources, and also the cost of investment in new and renewable energy is very costly and uneconomical. In the industrial sector, especially energy-intensive industries such as steel, finding appropriate solutions to update the technology, production and reduce energy consumption in this sector is very important. The main aim of this study is the identification an...

In this paper, the compactly supported orthonormal symmetrical wavelets is used to estimate non-uniformly sampled and non-gaussian noise corrupted load consumption signals for the purpose of load forecasting in a typical electrical utility network. Power load forecasting is an important function of utility management and present methods invariably rely heavily on past historical load curves whi...

This paper investigates the forecasting accuracy of the trimmed mean inflation rate of the Personal Consumption Expenditure (PCE) deflator. Earlier works have examined the forecasting ability of limited-influence estimators (trimmed means and the weighted median) of the Consumer Price Index but none have compared the weighted median and trimmed mean of the PCE. Also addressed is the systematic ...

$EVWUDFW The work is devoted to an application of artificial neural network (multilayer perceptron) and conditional stochastic simulations to electricity load forecasting in Russia. One of the problems is missing data and some important weather parameters (wind, cloudiness, precipitation, historical information). This gives rise to rather large forecasting errors with complex statistical struct...

While the literature has focused on large, industrial, or national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Since electricity consumption behavior may vary between households, we first build a feature universe, and then apply Correlationbased Feature Selection to select features...