Solar PV power forecasting at Yarmouk University using machine learning techniques

Renewable energy sources are considered ubiquitous and drive the energy revolution. Energy producers suffer from inconsistent electricity generation. They often struggled with the unpredictability of the weather. Thus, making it challenging to balance supply and demand. Technologies like artificial intelligence (AI) and machine learning are effective ways to forecast, distribute, andmanage renewable photovoltaic (PV) solar supplies. AI will make the energy forecasting system more connected, intelligent, reliable, and sustainable. AI can innovate how energy is used and help find solutions for decarbonizing energy systems. There are potential advantages to total energy forecasting. AI can support the growth and integration of PV solar energy. The article’s main objective is to use AI to forecast the output consumed power of the Yarmouk University PV solar system in Jordan. The total actual yield is 5548.96 MW h, and the performance ratio (PR) is 95.73%. Many techniques are used to predict the consumed solar power. The random forest model obtains the best results of root mean squared error and mean absolute error are 172.07 and 68.7, respectively. This accurate prediction allows for the maximum use of solar power and the minimal use of grid power. This work guides the operators to learn trends embedded in Yarmouk University’s historical data. These understood trends can be used to predict the consumption of solar power output. Thus, the control system and grid operators have advanced knowledge of the expected consumption of solar power at each hour of the day

A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network

Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. TwoWell-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work’s main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan’s current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results

Ketamine-based sedation use in mechanically ventilated critically ill patients with COVID-19: A multicenter cohort study

Backgrounds: Ketamine possesses analgesia, anti-inflammation, anticonvulsant, and neuroprotection properties. However, the evidence that supports its use in mechanically ventilated critically ill patients with COVID-19 is insufficient. The study's goal was to assess ketamine's effectiveness and safety in critically ill, mechanically ventilated (MV) patients with COVID-19. Methods: Adult critically ill patients with COVID-19 were included in a multicenter retrospective-prospective cohort study. Patients admitted between March 1, 2020, and July 31, 2021, to five ICUs in Saudi Arabia were included. Eligible patients who required MV within 24 hours of ICU admission were divided into two sub-cohort groups based on their use of ketamine (Control vs. Ketamine). The primary outcome was the length of stay (LOS) in the hospital. P/F ratio differences, lactic acid normalization, MV duration, and mortality were considered secondary outcomes. Propensity score (PS) matching was used (1:2 ratio) based on the selected criteria. Results: In total, 1,130 patients met the eligibility criteria. Among these, 1036 patients (91.7 %) were in the control group, whereas 94 patients (8.3 %) received ketamine. The total number of patients after PS matching, was 264 patients, including 88 patients (33.3 %) who received ketamine. The ketamine group's LOS was significantly lower (beta coefficient (95 % CI): −0.26 (−0.45, −0.07), P = 0.008). Furthermore, the PaO2/FiO2 ratio significantly improved 24 hours after the start of ketamine treatment compared to the pre-treatment period (6 hours) (124.9 (92.1, 184.5) vs. 106 (73.1, 129.3; P = 0.002). Additionally, the ketamine group had a substantially shorter mean time for lactic acid normalization (beta coefficient (95 % CI): −1.55 (−2.42, −0.69), P 0.01). However, there were no significant differences in the duration of MV or mortality. Conclusions: Ketamine-based sedation was associated with lower hospital LOS and faster lactic acid normalization but no mortality benefits in critically ill patients with COVID-19. Thus, larger prospective studies are recommended to assess the safety and effectiveness of ketamine as a sedative in critically ill adult patients

A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network

Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. Two Well-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work’s main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan’s current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results

Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy

In recent decades, the energy market around the world has been reshaped to accommodate the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’ constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system is performed using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of simulation and statistical results of EO with other optimization techniques showed that EO is more effective and superior and provides the lowest optimization value in term of electric power generation, real power loss, emission index and voltage deviation

Impact of wheeling photovoltaic system on distribution low voltage feeder

Renewable energy generation (REG) has been the main concern for policymakers, governments, industries, researchers, and other energy agencies in leaser fossil fuel and polluted environments. Nowadays, distributed photovoltaic (DPV) integration is a vital substitute for installing new substation feeders. DPV is used to reduce distribution loading. Thus, it can be analyzed as an optimization problem addressing multiple objectives. This optimization can help select the technology, siting, and sizing of DPV. This work studies automatic voltage regulator (AVR) and capacitor bank placement to mitigate the DPV penetration effect in the distribution low voltage feeder. A domestic distribution load of Irbid-Jordan is selected to perform this study. Based on the downstream 0.4 kV data, a feeder profile is monitored as live data via multichannel meters. The operating characteristic of the DPV system, such as losses, voltage profile, variation in voltage, and reliability, depend on the sizing and siting of the DPV. The size and siting of DPV are optimized using iterative techniques. The size of the DPV varies from 10 kW to 100 kW, and its location varies from 100 m to 1000 m. Different scenarios are simulated before and after PV penetration, installing an AVR and a low-voltage capacitor bank. Results show that an optimum installation is at a 7 kW rating and 700 m far from the main feeder with a loss reduction is (8167 kWh, 25.96%, 571.7 JD). A maximum permissible generation curve is obtained for this wheeling system. Thus, the optimum installation of DPV reduces the system losses and enhances the system's reliability and voltage profile. The system is modeled and studied via CYME software

Statistical Modeling of the Determinants Driving the Electricity Demand in Jordan

The paper introduces a statistical model that connects the electrical demand in Jordan with several determinants that have a direct impact on the electrical consumption and load profile during the study period from 2007 to 2020. The period was selected as it is characterized by several global events that directly impacted Jordan’s economy and energy sustainability in Jordan, such as the Arab spring protests, the civil war in Syria, and the global financial crises. Many determinants that are used in the regression analysis imply the ambient temperature, day of the week, population, gross domestic product (GDP), oil price, and technological factors related to renewable energy projects. Results show that temperature and population positively impact the demand, whereas GPD, population, oil prices, and renewable energy negatively impact the electricity demand. The results obtained from backcasting regression analysis for the hourly 4745 data set covering 13 years period reveals reasonable error metrics with MAE, MAPE, and RMSE values of 134, 6.3% and 2.76%, respectively. The government must encourage investments to exploit and explore the massive potential of available energy resources such as oil, natural gas, oil shale, and uranium to resolve the problems related to the high global oil prices and high dependency on imported energy. Also, it is required to enable the transition from fossil fuels to renewable energy through financial incentives and tax exemption to encourage investments in clean energy, rebuild a new traffic system showing the volatile electricity prices, which are still unknown and finally remove obstacles and facilitate the ongoing projects, reaching a state of stakeholder buy-in engaging with the projects

IoT Applications in Wind Energy Conversion Systems

Renewable energy reliability has been the main agenda nowadays, where the internet of things (IoT) is a crucial research direction with a lot of opportunities for improvement and challenging work. Data obtained from IoT is converted into actionable information to improve wind turbine performance, driving wind energy cost down and reducing risk. However, the implementation in IoT is a challenging task because the wind turbine system level and component level need real-time control. So, this paper is dedicated to investigating wind resource assessment and lifetime estimation of wind power modules using IoT. To illustrate this issue, a model is built with sub-models of an aerodynamic rotor connected directly to a multi-pole variable speed permanent magnet synchronous generator (PMSG) with variable speed control, pitch angle control and full-scale converter connected to the grid. Besides, a large number of various sensors for measurement of wind parameters are integrated with IoT. Simulations are constructed with Matlab/Simulink and IoT ’Thingspeak’ Mathworks web service. IoT has proved to increase the reliability of measurement strategies, monitoring accuracy, and quality assurance

Daily load curve prediction for Jordan based on statistical techniques

The article proposes a mathematical prediction model for daily load curves (DLCs) in Jordan from 2023–2050. The historical hourly peak loads based on the growth rate statistical method in 1994–2020 and the annual forecasted peak loads during the morning and evening periods taken from the long-term load forecast (LTLF) study of National Electric Power Company (NEPCO) during 2022–2050 are employed in the prediction model. The results show that the actual hourly growth rates, the annual forecasted growth rates, and the hourly peak loads in the reference year 2022 are the main input variables used in the prediction formula. The LTLF study conducted by NEPCO employs various sophisticated methods depending on the end-user sectorial electricity consumption that imply an econometric approach, market survey, and Gomprtz extrapolation techniques. The peak load in Jordan relies upon several climatic and nonclimatic variables, implying the ambient temperature, gross domestic product, income, demographic, urbanization, electricity tariff, average oil prices, and other factors related to technology and new aspects of energy saving and space heating/cooling systems, the DLC in Jordan is variable and changing from year to year. The proposed model considers a variation in the future DLC and suggests three different scenarios of DLC’s prediction based on the time occurrence of the peak load: the first is the daytime peak occurrence scenario, the second is the evening peak occurrence scenario, and finally is the daytime and evening peaks may be close to each other

Three-Phase Feeder Load Balancing Based Optimized Neural Network Using Smart Meters

The electricity distribution system is the coupling point between the utility and the end-user. Typically, these systems have unbalanced feeders due to the variety of customers’ behaviors. Some significant problems occur; the unbalanced loads increase the operational cost and system investment. In radial distribution systems, swapping loads between the three phases is the most effective method for phase balancing. It is performed manually and subjected to load flow equations, capacity, and voltage constraints. Recently, due to smart grids and automated networks, dynamic phase balancing received more attention, thus swapping the loads between the three phases automatically when unbalance exceeds permissible limits by using a remote-controlled phase switch selector/controller. Automatic feeder reconfiguration and phase balancing eliminates the service interruption, enhances energy restoration, and minimize losses. In this paper, a case study from the Irbid district electricity company (IDECO) is presented. Optimal reconfiguration of phase balancing using three techniques: feed-forward back-propagation neural network (FFBPNN), radial basis function neural network (RBFNN), and a hybrid are proposed to control the switching sequence for each connected load. The comparison shows that the hybrid technique yields the best performance. This work is simulated using MATLAB and C programming language