Smart load prediction analysis for distributed power network of Holiday Cabins in Norwegian rural area
Abstract The Norwegian rural distributed power network is mainly designed for Holiday Cabins with limited electrical loading capacity. Load prediction analysis, within such type of network, is necessary for effective operation and to manage the increasing demand of new appliances (e. g. electric vehicles and heat pumps). In this paper, load prediction of a distributed power network (i.e. a typical Norwegian rural area power network of 125 cottages with 478 kW peak demand) is carried out using regression analysis techniques for establishing autocorrelations and correlations among weather parameters and occurrence time in the period of 2014–2018. In this study, the regression analysis for loa…
Comparing Recurrent Neural Networks using Principal Component Analysis for Electrical Load Predictions
Electrical demand forecasting is essential for power generation capacity planning and integrating environment-friendly energy sources. In addition, load predictions will help in developing demand-side management in coordination with renewable power generation. Meteorological conditions influence urban area load pattern; therefore, it is vital to include weather parameters for load predictions. Machine Learning algorithms can effectively be used for electrical load predictions considering impact of external parameters. This paper explores and compares the basic Recurrent Neural Networks (RNN); Simple Recurrent Neural Networks (Vanilla RNN), Gated Recurrent Units (GRU), and Long Short-Term Me…
Relative evaluation of regression tools for urban area electrical energy demand forecasting
Abstract Load forecasting is the most fundamental application in Smart-Grid, which provides essential input to Demand Response, Topology Optimization and Abnormally Detection, facilitating the integration of intermittent clean energy sources. In this work, several regression tools are analyzed using larger datasets for urban area electrical load forecasting. The regression tools which are used are Random Forest Regressor, k-Nearest Neighbour Regressor and Linear Regressor. This work explores the use of regression tool for regional electric load forecasting by correlating lower distinctive categorical level (season, day of the week) and weather parameters. The regression analysis has been do…
Evaluating Anomaly Detection Algorithms through different Grid scenarios using k-Nearest Neighbor, iforest and Local Outlier Factor
Author's accepted manuscript © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage. The available advanced information and communicating platform and computational capability renders smart grid prone to attacks with extreme social, financial an…
Deregulated Electric Energy Price Forecasting in NordPool Market using Regression Techniques
Deregulated electricity market day-ahead electrical energy price forecasting is important. It is influenced by external parameters and it is a complicated function. In this work two neighboring regions in the NordPool market are analyzed to provide day-ahead electrical price forecasting using regression techniques. The characteristics of the NordPool market trading behavior leads to unanticipated price peaks at daily, weekly and annual level. The considered two Nordic regions have different energy generation sources (e.g Norway has controllable hydro power, Denmark has non-controllable wind-power) therefore day-ahead electrical energy price forecasting in deregulated market for these two ne…
Load Demand Analysis of Nordic Rural Area with Holiday Resorts for Network Capacity Planning
Most of the Nordic holiday resorts are in rural area with low capacity distributed network. The rural area network is weak and needs capacity expansion planning as the load demand of this area are going to increase due to penetration of electric vehicles and heat pumps. Such type of rural network can also be operated as a micro-grid, and therefore load analysis is required for appropriate operation. The load analysis will also be useful for finding proper sizing of distributed energy resources including energy storage. In this work, load demand analysis of a typical Nordic holiday resorts, connected in rural grid, is presented to find out the load variation during the usage periods. The loa…