Creating optimized machine learning pipelines for PV power generation forecasting using the grid search and tree-based pipeline optimization tool

AbstractDemand for electric power, especially amidst limited fossil fuel-based generation capacity, has elevated renewable energy sources to a forefront solution for the growing energy needs. Solar energy, a key renewable source through photovoltaic (PV) panels, faces challenges such as intermittency and non-dispatchability. Thus, recent research has focused on developing programs to predict near-future solar energy generation, with machine learning being a pivotal approach. This article details the creation of an effective machine-learning pipeline for predicting future hourly power generation based on weather data (e.g. temperature, humidity, irradiance). The pipeline, aimed at a scheduling system in a farm equipped with a Solar Power System (SPS) in Al-Salt, Jordan, was optimized using Genetic Algorithm and Grid Search methods. The objective of this article is to create an optimal pipeline with minimal loss. The evaluation shows that ensemble regressors, especially Gradient Boosting Regressors, are effective. This is evidenced in the grid search pipeline, which outperformed the TPOT optimization pipeline-derived pipeline, the latter including stacked ensemble regressors and sequential preprocessors

Baseline carbon emission assessment in water utilities in Jordan using ECAM tool

This study presents a baseline assessment of carbon emissions in water utilities in Madaba, Jordan. The Energy Performance and Carbon Emissions Assessment and Monitoring Tool (ECAM) is applied in the present study in order to reduce indirect and direct emissions. Input data for the assessment included inter alia, population, water volumes, energy consumption, and type of wastewater treatment. The methodology focuses on the greenhouse gas (GHG) emissions and energy use that is directly associated with the utility operations covering the whole water cycle. The ECAM's Quick Assessment revealed that 89.7% of the energy is consumed in abstraction and distribution systems of water supply, whereas wastewater collection, treatment, and discharge consumed only 10.3% in Madaba. The detailed ECAM tool assessment results showed that total GHG emissions from the entire water and wastewater system in Madaba are approximately 28.122 million kg CO2/year. The water supply is the major contributor to GHG accounting for 62.4%, while 37.6% of GHG emissions result from sewage treatment, and are associated with treatment process requirements considered in this work, in addition to sludge transport from septic tanks to the wastewater treatment plant. The findings of this work can help the utility to undertake energy efficiency and GHG reduction measures.Miyahuna Company [12.9046.9-006.00]The authors are grateful for the collaboration provided by Miyahuna Company staff in the stage of site visits, data gathering and analysis, and consultative meetings undertaken throughout the WaCCliM project (No. 12.9046.9-006.00) period. This work is co-published by the Water, Energy, and Environment Center, University of Jordan research team and GIZ as part of the Project ` Water and Wastewater Companies for Climate Mitigation WaCCliM' activities. The Project is implemented by GIZ in partnership with the International Water Association and on behalf of the German the Federal Ministry of the Environment, Nature Conservation and Nuclear Safety (BMU). All rights for the content of this paper are reserved for GIZ, the views presented are entirely the responsibility of the authors

Creating optimized machine learning pipelines for PV power generation forecasting using the grid search and tree-based pipeline optimization tool

Demand for electric power, especially amidst limited fossil fuel-based generation capacity, has elevated renewable energy sources to a forefront solution for the growing energy needs. Solar energy, a key renewable source through photovoltaic (PV) panels, faces challenges such as intermittency and non-dispatchability. Thus, recent research has focused on developing programs to predict near-future solar energy generation, with machine learning being a pivotal approach. This article details the creation of an effective machine-learning pipeline for predicting future hourly power generation based on weather data (e.g. temperature, humidity, irradiance). The pipeline, aimed at a scheduling system in a farm equipped with a Solar Power System (SPS) in Al-Salt, Jordan, was optimized using Genetic Algorithm and Grid Search methods. The objective of this article is to create an optimal pipeline with minimal loss. The evaluation shows that ensemble regressors, especially Gradient Boosting Regressors, are effective. This is evidenced in the grid search pipeline, which outperformed the TPOT optimization pipeline-derived pipeline, the latter including stacked ensemble regressors and sequential preprocessors.</p

Assessment of On-Site Treatment Process of Institutional Building’s Wastewater

This study is conducted to investigate the characteristics of outflow wastewater of the 1 m3 on-site wastewater treatment unit on the basis of the testing and measurement data of the samples that were taken during the study monitored period (August 2017 to January 2018). For this purpose, samples were taken on a weekly basis from the treated wastewater effluent and five quality parameters (biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), pH, E-coli counts) were monitored and measured. The average values of the five parameters were compared with the Jordanian standard maximum values, and water reuse in irrigation of plants classifications have been assessed and investigated. Average values of BOD, COD, TSS, pH, and E-coli in treated wastewater were 11 mg/L, 104 mg/L, 15 mg/L, 7.51, and 387 counts, respectively. The installation of in-line ultraviolet (UV) unit in recirculating delivery system played a vital role in the reduction of counts far below the permissible maximum level (1000 counts). Based on national and international standards and criteria, results showed that the treated wastewater is suitable for the irrigation of two classifications of plants: (i) Fruit trees, road-green sides outside cities, and green landscape; (ii) Crops, commercial crops, and forest trees. Hence, such very low water flow rate treatment system can be utilized in refugees’ camps and water scarce residential areas in Jordan

Synthesis and characterization of ZrFe2O4@SiO2@Ade-Pd as a novel, recyclable, green, and versatile catalyst for Buchwald–Hartwig and Suzuki–Miyaura cross-coupling reactions

Abstract This work presents the simple synthesis of a green and novel Palladium based magnetic nanocatalyst with effective catalytic properties and reusability. These heterogeneous catalysts were prepared by the anchoring of Pd(0) on the surface of ZrFe2O4 MNPs coated with a di-substituted adenine (Ade) compound as a green linker. The as-synthesized ZrFe2O4@SiO2@Ade-Pd MNPs were methodically characterized over different physicochemical measures like VSM, EDX, Map, SEM, TEM, ICP, and FT-IR analysis. The catalytic activity of ZrFe2O4@SiO2@Ade-Pd was carefully examined for the room-temperature Carbon–Carbon coupling reaction in acetonitrile as a solvent. It is worth noting that the synthesized solid catalyst can be easily recovered with a bar magnet and reused for five cycles without decrease of catalytic activity