As an important part of the power system, load forecasting directly affects national economy. Small improvements in forecasts can save millions dollars for industry. Therefore, improving accuracy has always been pursuing goal a system. Based on this goal, paper proposes novel connection, dense average which outputs all preceding layers are averaged as input next layer feed-forward fashion. Dens...

Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on electricity grid. As transitions towards less reliable renewable generation, smart meters will prove vital component facilitate these tasks. However, meter adoption low among privacy-conscious consumers that fear intrusion upon their fine-grained consumptio...

Forecasting short-term electrical load is very important so that the quality of power supplied can be maintained properly. The study was conducted to measure results forecasting based on parameter estimates and presentation time series data. It manage stationary data, both in terms mean variance. Data done by determining value variance through Box-Cox transformation method ACF PACF plots. This ...

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based ...

<span>Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole can be increased with proper planning organization. Residential load is indispensable due to its increasing smart grid environment. Nowadays, meters deployed at residential level for collecting historical data consumption residents. Although employment ensures large availab...

This paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets — one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian electric utility. It was ...

Forecasting load is an integral part of the planning, operation, and control power systems. This paper a research effort aimed at developing better energy demand forecasting models for dispatch centers (LDCs) in Indian states as ambitious project utilizing artificial intelligence-based models. In this paper, we present half hourly method management system that will be used 33 /11 kV 0.415 subst...