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.

This paper presents a detailed study on wind power forecasting using a long short-term memory (LSTM) model for single turbine wind farm. The research integrates technical modeling, predictive modeling, and heat map analysis to improve forecasting accuracy. Wind power is predicted based on current weather conditions and historical data using multivariate time series forecasting with LSTM, implemented via the Keras-library. Various look-back values are tested to enhance prediction precision. With a 70% training and 30% testing split, the model achieves an MSE of 27.781, RMSE of 5.271, MAE of 3.281, and variance of 0.886. A 60% training and 40% testing split slightly improves performance. During the prediction phase, the LSTM model forecasts power output without future weather inputs. Optimal look-back periods and neuron numbers are identified, resulting in MAPEs of 11.433% and 11.158% for 24 and 48 h of data, respectively. Optimization techniques, including adjustments to batch size, normalization, and the addition of dense layers, further improve forecasting accuracy. Notably, MAPE decreases from 92.53% to 87.14% in a monthly prediction scenario. Forward predictions for 24 h, 2 days, and 1 week result in MAPEs of 70.74%, 39.26%, and 51.48%, respectively. Additionally, a comparison of two LSTM-based models autoencoder-LSTM and FFT-encoder-decoder LSTM shows that the latter offers superior performance. This study demonstrates the strong potential of LSTM models for power forecasting and introduces innovative strategies for optimizing model performance. The combined use of technical and predictive models, heat map analysis, and LSTM architecture contributes significantly to the advancement of wind power prediction methodologies. Received: 22 May 2023 | Revised: 9 October 2023 | Accepted: 1 March 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Abdel Ali Mana: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Abdelmajid Jamil: Conceptualization, Validation, Writing – review & editing, Supervision, Project administration. Kamar Ouazzani: Conceptualization, Validation, Writing – review & editing, Supervision, Project administration. Writing – review & editing, Supervision, Project administration.

Despite growing interest in positive-energy and net-zero-energy buildings (NZEBs), few studies have addressed the integration of biobased construction with building-integrated photovoltaics (BIPV) under hot–dry climate conditions, particularly in Morocco and North Africa. This study fills this gap by presenting a simulation-based evaluation of energy performance and renewable energy integration strategies for a residential building in the Fes-Meknes region. Two structural configurations were compared using dynamic energy simulations in DesignBuilder/EnergyPlus, that is, a conventional concrete brick model and an eco-constructed alternative based on biobased wooden materials. Thus, the wooden construction reduced annual energy consumption by 33.3% and operational CO₂ emissions by 50% due to enhanced thermal insulation and moisture-regulating properties. Then multiple configurations of the solar energy systems were analysed, and an optimal hybrid off-grid hybrid system combining rooftop photovoltaic, BIPV, and lithium-ion battery storage achieved a 100% renewable energy fraction with an annual output of 12,390 kWh. While the system incurs a higher net present cost of $45,708 USD, it ensures full grid independence, lowers the electricity cost to $0.70/kWh, and improves occupant comfort. The novelty of this work lies in its integrated approach, which combines biobased construction, lifecycle-informed energy modelling, and HOMER-optimised PV/BIPV systems tailored to a hot, dry climate. The study provides a replicable framework for designing NZEBs in Morocco and similar arid regions, supporting the low-carbon transition and informing policy, planning, and sustainable construction strategies.