Sahiba Fareed's research while affiliated with COMSATS University Islamabad and other places

Publications (11)

Conference Paper
Full-text available
Conventional grid moves towards Smart Grid (SG). In conventional grids, electricity is wasted in generation-transmissions-distribution, and communication is in one direction only. SG is introduced to solve prior issues. In SG, there are no restrictions, and communication is bi-directional. Electricity forecasting plays a significant role in SG to e...
Chapter
High price fluctuations have a direct impact on electricity market. Thus, accurate and plausible price forecasts have been implemented to mitigate the consequences of price dynamics. This paper proposes two techniques to deal with the Electricity Price Forecasting (EPF) problem. Firstly, Convolutional Neural Network (CNN) model is used to predict t...
Chapter
Conventional grid moves towards Smart Grid (SG). In conventional grids, electricity is wasted in generation-transmissions-distribution, and communication is in one direction only. SG is introduced to solve prior issues. In SG, there are no restrictions, and communication is bi-directional. Electricity forecasting plays a significant role in SG to e...
Chapter
Traditional grid moves toward Smart Grid (SG). In traditional grids, electricity was wasted in generation-transmission-distribution. SG is introduced to solve prior issues. In smart grids, how to utilize massive smart meter’s data in order to improve and promote the efficiency and viability of both generation and demand side is a compelling issue....
Conference Paper
Full-text available
High price fluctuations have a direct impact on electricity market. Thus, accurate and plausible price forecasts have been implemented to mitigate the consequences of price dynamics. This paper proposes two techniques to deal with the Electricity Price Forecasting (EPF) problem. Firstly, Convolutional Neural Network (CNN) model is used to predict t...
Conference Paper
Full-text available
Conventional grid moves towards Smart Grid (SG). In conventional grids, electricity is wasted in generation-transmissions-distribution, and communication is in one direction only. SG is introduced to solve prior issues. In SG, there are no restrictions, and communication is bi-directional. Electricity forecasting plays a significant role in SG to e...
Conference Paper
Full-text available
Traditional grid moves toward Smart Grid (SG). In traditional grids, electricity was wasted in generation-transmission-distribution. SG is introduced to solve prior issues. In smart grids, how to utilize massive smart meter's data in order to improve and promote the efficiency and viability of both generation and demand side is a compelling issue....
Conference Paper
Full-text available
Smart grid (SG) is bringing revolutionary changes in the electric power system. SG is supposed to provide economic, social, and environmental benefits for many stakeholders. A smart meter is an essential part of the SG. Data acquisition, transmission, processing, and interpretation are factors to determine the success of smart meters due to the exc...
Conference Paper
Full-text available
Due to increase in electronic appliances, electricity is becoming basic necessity of life. Consumption of electricity depends on various factors like temperature , wind, humidity, weekend, working days and season. In electricity load forecasting, many researchers perform data analysis on electricity data provided by utilities to extract meaningful...
Chapter
Due to increase in electronic appliances, electricity is becoming basic necessity of life. Consumption of electricity depends on various factors like temperature, wind, humidity, weekend, working days and season. In electricity load forecasting, many researchers perform data analysis on electricity data provided by utilities to extract meaningful i...
Chapter
Smart grid (SG) is bringing revolutionary changes in the electric power system. SG is supposed to provide economic, social, and environmental benefits for many stakeholders. A smart meter is an essential part of the SG. Data acquisition, transmission, processing, and interpretation are factors to determine the success of smart meters due to the exc...

Citations

... In other words, at any given time, the forecasting system for an hour later predicts a quantity based on the existing data [73][74][75][76][77][78][79][80][81][82][83]. This model is not very efficient, and little has been done for the price [84][85][86][87][88][89][90]. In the short-term or day-ahead forecasting method, the forecast is usually done for the next day. ...
... These interventions slightly brought electricity demand close to its supply, while at the same time reasonable reserve margins were being maintained. In energy sectors risk denotes a probability distribution of future returns; hence, uncertainty is considered as a broader concept that incorporates indistinctness about the parameters of this probability distribution Babatunde et al. (2020) and Anwar et al. (2020). ...
... After 2021, some researchers have also proposed a kernel-based extreme learning machine power price prediction model [20,21]. Recently, with the development of deep learning, electricity price prediction models based on deep learning have been continuously proposed, including BP, CNN, RNN, etc. [22][23][24]. Recently, the LSTM-based electricity price prediction method has received widespread attention because of its excellent performance. ...
... Liu et al. [15] also find that a sparse encoding network can improve the forecast for an LSTM at the household-level. Naeem et al. [90] develop a day-ahead load forecast of an Australian network-grid using an Ensemble Empirical Mode Decomposition (EEMD) to decompose the signal into Intrinsic Mode Functions (IMF) and residuals. These modes and residuals are passed onto a Denoising Auto Encoder (DAE) for feature extraction. ...
... Forecasting of electricity plays an important role in the smart grid to minimize operational costs and effective management. Load and price prediction provide future trends (Arif et al., 2020). The latest developments in information technologies and the accessibility of bulky and varied data in power grids are opening the ways for the deployment of intelligent algorithms. ...
... The growing need for energy demand caused by population growth, and the rapid increase in electronic appliances in the residential areas, drives further research in development of effective strategies of energy management for keeping the balance between supply and demand with an acceptable cost [113], [114]. In addition, extensive and irregular usage of electric appliances leads to obtain an irregular scheme of daily consumption resulting in imbalance electricity load over specific time intervals especially in the On-peak periods and destabilizes the utility. ...