SEARCH RESULT

This paper addresses the challenges of load forecasting that occur due to the complex nature of load in different predicting horizons and as well as the total consumption within these horizons. It is not often easy to accurately fit the several complex factors that are faced with demand for electricity into the predicting models. More so, due to the dynamic nature of these complex factors (i.e., temperature, humidity and other factors that influence consumption), it is difficult to derive an accurate demand forecast based on these parameters. As a consequence, a model that uses hourly electricity loads and temperature data to forecast the next hourly loads is proposed. The model is based on modified entropy mutual information based feature selection to remove irrelevancy and redundancy from the dataset. Conditional restricted Boltzmann machine (CRBM) is investigated to perform load forecasting; accuracy and convergence are improved to reduce the CRBM’s forecast error via a Jaya based meta-heuristic optimization algorithm. The proposed model is implemented on the publicly available dataset of GEFCom2012 of the US utility. Comparative analysis is carried out on an existing accurate, fast converging shortterm load forecasting (AFC-STLF) model since it has a similar architecture to the proposed model. Simulation results confirm that the proposed model improves the accuracy up to 56.32% as compared to 43.67% of AFC-STLF. Besides, the proposed model reduces the average execution time up to 53.87% as compared to 46.12% of AFC-STLF.

International Conference on Cyber Security and Computer Science
ICONCS

Omaji Samuel Nadeem Javaid Asma Rafique

Home energy management systems (HEMSs) based on demand response (DR) synergized with renewable energy sources (RESs) and energy storage systems (ESSs) optimal dispatch (DRSREOD) are used to implement demand-side management in homes. Such HEMSs benefit the consumer and the utility by reducing energy bills, reducing peak demands, achieving overall energy savings and enabling the sale of surplus energy. Further, a drastically rising demand of electricity has forced a number of utilities in developing countries to impose large-scale load sheddings (LSDs). A HEMS based on DRSREOD integrated with an LSD-compensating dispatchable generator (LDG) (DRSREODLDG) ensures an uninterrupted supply of power for the consumers subjected to LSD. The LDG operation to compensate the interrupted supply of power during the LSD hours; however, accompanies the release of GHGs emissions as well that need to be minimized to conserve the environment. A 3-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of DRSREODLDG-based HEMS. The method provides the tradeoffs between the net cost of energy (CEnet) to be paid by the consumer, the time-based discomfort (TBD) due to shifting of home appliances (HAs) to participate in the HEMS operation and minimal emissions (TEMiss) from the local LDG. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. The surface fit is developed using polynomial models for regression based on the least sum of squared errors and selected solutions are classified for critical tradeoff analysis to enable the consumer by choosing the best option and consulting a diverse set of eco-efficient tradeoffs between CEnet, TBD and TEMiss.

International Conference on Cyber Security and Computer Science
ICONCS

Bilal Hussain Nadeem Javaid Qadeer-ul Hasan Yüksel Çelik Asma Rafique

Home energy management systems (HEMSs) based on demand response (DR) synergized with renewable energy sources (RESs) and energy storage systems (ESSs) optimal dispatch (DRSREOD) are used to implement demand-side management in homes. Such HEMSs benefit the consumer and the utility by reducing energy bills, reducing peak demands, achieving overall energy savings and enabling the sale of surplus energy. Further, a drastically rising demand of electricity has forced a number of utilities in developing countries to impose large-scale load sheddings (LSDs). A HEMS based on DRSREOD integrated with an LSD-compensating dispatchable generator (LDG) (DRSREODLDG) ensures an uninterrupted supply of power for the consumers subjected to LSD. The LDG operation to compensate the interrupted supply of power during the LSD hours; however, accompanies the release of GHGs emissions as well that need to be minimized to conserve the environment. A 3-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of DRSREODLDG-based HEMS. The method provides the tradeoffs between the net cost of energy (CEnet) to be paid by the consumer, the time-based discomfort (TBD) due to shifting of home appliances (HAs) to participate in the HEMS operation and minimal emissions (TEMiss) from the local LDG. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. The surface fit is developed using polynomial models for regression based on the least sum of squared errors and selected solutions are classified for critical tradeoff analysis to enable the consumer by choosing the best option and consulting a diverse set of eco-efficient tradeoffs between CEnet, TBD and TEMiss.

International Conference on Cyber Security and Computer Science
ICONCS

Bilal Hussain Nadeem Javaid Qadeer-ul Hasan Yüksel Çelik Asma Rafique

Recently big data analytics are gaining popularity in the energy management systems (EMS). The EMS are responsible for controlling, optimization and managing the energy market operations. Energy consumption forecasting plays a key role in EMS and helps in generation planning, management and energy conversation. A large amount of data is being collected by the smart meters on daily basis. Big data analytics can help in achieving insights for smart energy management. Several prediction methods are proposed for energy consumption forecasting. This study explores the state-of-the-art forecasting methods. The studied forecasting methods are classified into two major categories: (i) univariate (time series) forecasting models and (ii) multivariate forecasting models. The strengths and limitations of studied methods are discussed. Comparative anlysis of these methods is also done in this survey. Furthermore, the forecasting techniques are reviewed from the aspects of big data and conventional data. Based on this survey, the gaps in the existing research are identified and future directions are described.

International Conference on Cyber Security and Computer Science
ICONCS

Sana Mujeeb Nadeem Javaid Sakeena Javaid Asma Rafique Manzoor Ilahi

Smart grid technologies ensures reliability, availability and efficiency of energy which contribute in economic and environmental benefits. On other hand, communities have smart homes with private energy backups however, unification of these backups can beneficial for the community. A community consists of certain number of smart homes (SH) which have their own battery based energy storage system. In this paper, 12 smart communities are connected with 12 fog computing environment for power economy sharing within the community. Each community has 10 smart homes with battery bases energy storage system. These communities are evaluated for load and cost profiles with three scenarios; SHs without storage system, SHs with storage system for individual SH requirements and SHs with unified energy storage system (unified-ESS). Unified-ESS is formed with the help of home and fog based agents. Simulations show that, unfied-ESS is efficient to have reduced cost for SHs within the community.

International Conference on Cyber Security and Computer Science
ICONCS

Rasool Bukhsh Nadeem Javaid Asma Rafique

In the modern day world and with growing technology, load forecasting is taken as the significant concerns in the power systems and energy management. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has proposed different techniques for demand load forecasting like neural networks, fuzzy methods, Na ¨ ıve Bayes and regression based techniques. This paper proposes a novel knowledge based system for short-term load forecasting. The proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Besides, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed model is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the proposed system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on yearly basis. Furthermore, the proposed knowledge based system gives more efficient outcomes for demand load forecasting.

International Conference on Cyber Security and Computer Science
ICONCS

Mahnoor Khan Nadeem Javaid Yüksel Çelik Asma Rafique