Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

Abstract

The rapid growth of hybrid renewable Distributed Energy Resources (DERs) generation possess various challenges with inaccurate forecast models in stochastic power systems. The prime objective of this research is to maximum utilization of scheduled power from hybrid renewable based DERs to maintain the load-demand profile with reduce distributed grid burden. The proposed mixed input-based cascaded artificial neural network [Formula: see text] is realized for the prediction of a short-term based hourly solar irradiance and wind speed. The testing approach is performed through a historical hourly dataset of the proposed site. Further, the normalized data sets are divided into hourly-based samples for validating the load demand power with respect to the variation in metrological data. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) model is simulated for short-term power demand prediction. This adaptive methodology is an effective approach for load-demand management which is based on cross-entropy. It also confirmed that during testing, the forecasting mean error and cross-entropy are less than 5% under a specific time slap of an individual day. The regression analysis is performed through the time series fitting simulation tool at different time horizons. The performance evaluation of the designed model is compared with the multi-layer perceptron model. Simulation results display the proposed mixed input-based cascaded system has enhanced accuracy and optimal performance than the multi-output correlated perceptron model.

Intelligent energy management scheme‐based coordinated control for reducing peak load in grid‐connected photovoltaic‐powered electric vehicle charging stations

“…The ANFIS controller model examined in this section is utilized to forecast and control the power flow sharing between the DG system and BSS which is based on the historical EV charging load data set [55,56]. The EVCS was controlled for 7 days with the BSS being recharged throughout the off-peak duration (if the desired SoC level reaches to a lower value).…”

An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage

“…The more modern approaches come from Deep Learning, as they try to exploit the elevated number of parameters to capture complex temporal dependencies. Simple ANNs are adopted in [26], whereas Cascaded ANNs are proposed by [29] for resource forecasting. Fuzzy systems (ANFIS) are also employed in [21] and [29].…”

“…Simple ANNs are adopted in [26], whereas Cascaded ANNs are proposed by [29] for resource forecasting. Fuzzy systems (ANFIS) are also employed in [21] and [29].…”

“…Regarding the choice of the optimizer, in the field of Deep Learning SGD-based algorithms (Stochastic Gradient Descent) have become the de-facto standard. In the energy community they are fairly popular, adopted by [20], [21], [22], and [28]; however there are works that exploit other algorithms such as Levenberg-Marquardt in [26] and [29]. We chose to rely on SGD given its efficient implementations available in the PyTorch framework, and among its existing variants (SGD, Adam, AdamW, LARS..) we selected SGD by experimental validation.…”

Performance evaluation of grid connected solar powered microgrid: A case study

“…Setting up a solar PV plant on the floor isn't usually possible because of free and un-shaded land availability. Hence, the rooftop area may be applied efficaciously for power generation by putting in a solar PV plant (Milosavljevic et al, 2015;Dalal et al, 2021;Amir and Haque, 2022). Various tasks have been taken with the aid of government and private organizations to install solar PV plants on the roof of any of the government workplace buildings, houses, personal establishments and authorities, and personal workstations to address the latest power disaster and growth in tariff charges.…”