Toward a Sustainable Agriculture in Morocco Based on Standalone PV Pumping Systems: A Comprehensive Approach

Mana, A.A.; Allouhi, A.; Ouazzani, K.; Jamil, A.

doi:10.1007/978-3-030-64565-6_14

Cited by 7 publications

(4 citation statements)

References 63 publications

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“…Statistical methods have been used for wind power prediction but may not provide accurate results due to the erratic nature of wind speed. Machine learning methods, on the other hand, establish nonlinear relationships using models like the Support Vector Machine (SVM) or the more powerful Deep Neural Network (DNN) (Mana et al, 2023). DNN, particularly Recurrent Neural Networks (RNN) like Long Short-Term Memory (LSTM), have shown great success in learning feature representations and improving prediction accuracy for time series data (Ahmed et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…These models aim to improve the accuracy of predictions by considering seasonality and other influencing parameters specific to each location (Hu et al, 2022). Several studies have explored different forecasting models for wind speed and power prediction, including SVM, wavelet-based approaches, neural networks, and ensemble machine learning techniques (Mana et al, 2023). These models have demonstrated varying levels of accuracy, with a mean absolute percentage error (MAPE) ranging from 2.5% to 18%.…”

Section: Introductionmentioning

confidence: 99%

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Winds of Change: Enhancing Wind Power Generation Forecasting with LSTM Models and Advanced Techniques in a Single Turbine Wind Farm

Mana

2024

AIA

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This article presents a comprehensive analysis of wind power generation using a Long Short-Term Memory (LSTM) model in the context of a wind farm with a single turbine. The novelty of the research lies in the integration of a technical model, predictive model, heat map analysis, and LSTM architecture to enhance forecasting accuracy. The performance of the forecasted model is evaluated using standard metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). In the estimation phase, wind power generation is predicted based on current weather conditions and historical trends. Leveraging multivariate time series forecasting with LSTM through the Kera library, different look-back values are explored to optimise prediction accuracy. In particular, the study achieves favourable performance metrics, including MSE of 27.781, RMSE of 5.271, MAE of 3.281, and variance of 0.886 with a 70% training and 30% test split. Furthermore, a 60% training and 40% test split yield improved accuracy with MSE of 28.791, RMSE of 5.366, MAE of 3.376, and variance of 0.888. In the prediction phase, the LSTM model is employed to forecast power generation without relying on future weather information. Through various experiments, the optimal look-back period and number of neurones for the LSTM model are determined. Notably, the achieved mean absolute percent errors (MAPE) of 11.433% and 11.158% for 24 and 48 hours of data respectively showcase the impact of varying neuron counts on prediction accuracy. To further enhance predictive capabilities, optimisation techniques are implemented, involving adjustments to the LSTM model, including increased input batch size, batch normalisation, and dense layers. This optimisation leads to a substantial decrease in MAPE from 92.53% to 87.14% in a monthly prediction experiment. Furthermore, forward predictions are made for multiple days in the future, where LSTM successfully captures patterns and fluctuations, resulting in MAPEs of 70.74% for 24 hours, 39.26% for 2 days, and 51.48% for 1 week. Additionally, a comparison of two LSTM-based models, Autoencoder LSTM and FFT-Encoder-Decoder-LSTM, demonstrates the superior performance of the latter in terms of RMSE. This study emphasizes the potential of LSTM models in wind power generation prediction while introducing novel insights into optimizing model performance for accurate forecasting. The integration of technical and predictive models, along with heat map analysis and LSTM architecture, sets the groundwork for advancing wind power prediction methodologies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Winds of Change: Enhancing Wind Power Generation Forecasting with LSTM Models and Advanced Techniques in a Single Turbine Wind Farm

Mana

2024

AIA

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“…Following its commitments at the international level within the framework of the Earth Summits of Rio de Janeiro (1992) and Johannesburg (2002) and the relevant conventions, Morocco has put in place the foundations for sustainable development in the country by legal, political, institutional, and socio-economic new reforms. This process has been accompanied and reinforced by adopting an energy transition and the integration of SMEs (small and medium enterprises), creating more synergies [4], and developing the economic sector integrity in which industry with all its domains presents a primary axis because of the huge consumption and the significant emissions. In Morocco, industry accounts for 30% of CO 2 "energy" emissions, including 50% of direct emissions from the combustion of fossil fuels and 50% of indirect emissions related mainly to electricity use.…”

Section: Introduction 1literature Backgroundmentioning

confidence: 99%

A New Approach to Energy Transition in Morocco for Low Carbon and Sustainable Industry (Case of Textile Sector)

et al. 2022

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Morocco has resolutely committed to the green transition of its economy by opting for industry decarbonation, which now imposes itself as an essential access criterion to foreign markets. Intending to include energy efficiency in the leading players in energy-intensive industries, this paper has the main objective of contributing to a better understanding of the decarbonation plans potential impact, taking the example of solar energy integrating opportunities as an action for a thrifty, sustainable, and low carbon Moroccan industry. Indeed, the paper focuses on the industrial textile sector, such as the energy-intensive industry. This sector is the first employer and the most important industrial activity; it is also an icon and the oldest industry in Morocco. This study examines the energy, economic and environmental fallout, evaluating the productions, the investment and the CO2 emissions limit. Besides, the energy industrial sector is characterized by a strong dependence on fossil imports, which increases the energy factor and price. In this regard, several geographical sites and factories were studied under six climatic regional conditions, proposing the most optimal and sustainable configurations for each location and present models with scopes and levels of energy and environment gains and investments that can inspire the sector actors. Then the present work must install concepts by inspiring local factories, accompanying the national vision, and resizing the industrial ecology. In this paper, a power of 8.88 MW is the total power installed, which provides an annual total of 8484.65 tonnes of CO2, with an average payback time between 2.6 years and 4.5 years, and attractive economic parameters, with an LCOE of 0.034 $/kWh and $181,863 for the NPC, those outputs shows the importance of environmental gains that the generalization of this strategic vision can achieve.

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“…Regarding modelling and the management of energy and the irrigation system [30][31][32], Cuadros et al [33] proposed a procedure for estimating the size of a photovoltaic installation to feed a pumping system for drip irrigation of an olive grove in Southwestern Spain. They affirm that the main stages are as follows: determine the irrigation requirements of the specific farm, according to the characteristics of its soil type and the local climate; hydraulic analysis of the pumping system according to the depth of the aquifer and the height necessary to stabilize the pressure in the water distribution network; and determine the maximum photovoltaic power required for the farm, taking into account the overall performance of the photovoltaic-irrigation pump system.…”

Section: Introductionmentioning

confidence: 99%

Methodological Advances in the Design of Photovoltaic Irrigation

2021

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In this study, an algorithm has been developed that manages photovoltaic solar energy in such a manner that all generated power is delivered to the system formed by a pump and irrigation network with compensated emitters. The algorithm is based on the daily work matrix that is updated daily by considering water and energy balances. The algorithm determines an irrigation priority for the sectors of irrigation of the farm based on programmed irrigation time and water deficits in the soil and synchronises the energy produced with the energy requirement of the hydraulic system according to the priority set for each day, obtaining the combinations of irrigation sectors appropriate to the photovoltaic power available. It takes into account the increment/decrease in the pressure of the water distribution network in response to increases/decreases in photovoltaic energy by increasing/decreasing the rotational speed of the pump, thus increasing/decreasing the power transferred to the system. The application to a real case of a 10-hectare farm divided into four sectors implies an efficient use of the energy of 26.15% per year and savings in CO2 emissions of 6.29 tonnes per year.

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Toward a Sustainable Agriculture in Morocco Based on Standalone PV Pumping Systems: A Comprehensive Approach

Cited by 7 publications

References 63 publications

Winds of Change: Enhancing Wind Power Generation Forecasting with LSTM Models and Advanced Techniques in a Single Turbine Wind Farm

Winds of Change: Enhancing Wind Power Generation Forecasting with LSTM Models and Advanced Techniques in a Single Turbine Wind Farm

A New Approach to Energy Transition in Morocco for Low Carbon and Sustainable Industry (Case of Textile Sector)

Methodological Advances in the Design of Photovoltaic Irrigation

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