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Cycle 5 (2015 Deadline)

Seamless solar PV integration in Moroccan buildings

PI: Mounir Ghogho (, International University of Rabat
U.S. Partner: Paul Flikkema, Northern Arizona University
Project Dates: December 2016 - November 2021

Project Overview:

A high density of installed photovoltaic (PV) systems poses grid integration challenges. Excessive ramps and peaks of the injected PV power into low-voltage grids are some of the most important challenges that may destabilize the grid and even cause blackouts. The penetration of PV in buildings in Morocco is still very low. The potential for roof-mounted PV power generation in Morocco has been estimated to each 10TWh, representing 40% of the country's total electricity consumption. Despite this high potential, there are two main obstacles to the uptake of PV in buildings, the first being the capital investment cost, as no financial incentives are offered yet by the government, and the second being the conservative attitude of the national electricity utility company toward PV injection in the low-voltage grid.

To optimally design a battery-supported PV system, realistic household load profiles in Morocco must be used. The general objectives of this project are (1) to build a statistically significant dataset of household electricity consumption profiles in Morocco and make it available as open data to the scientific community; (2) to analyze the data using machine learning to assess and classify power quality at the household level; and (3) to develop scientific tools to investigate issues of sizing and operation of residential battery-supported PV systems in Moroccan settings from both the energetic and economic perspectives. Forecast-based control mechanisms will be devised for different scenarios. The results of the project should be of interest to the Moroccan Ministry of Energy and the national electric utility company. The International University of Rabat encourages through its technology transfer unit joint ventures with industry to turn research results into commercial products. The research team will explore this avenue at the end of the project. Overall, through their planned open data platform and outreach activities, the project team aims to contribute to the promotion of clean energy adoption in buildings and the modernization of the Moroccan electric grid.

January-March 2021 activities

During the reporting period, the team accomplished three main tasks:
  • Monitoring of individual and entire household energy consumption in an affluent neighborhood in Temara city. In addition, the team also monitored energy production from the PV panels installed on the roof of the premises. 
  • Developing a deep learning technique for short-term multi-horizon forecasting of residential electricity consumption. The team relied on the data from five different Moroccan households to assess the performance of the proposed technique. 
  • Comparative analysis of machine learning techniques for Global Horizontal Irradiance (GHI) forecasting.  
Summary of 2020 activities

During the reporting period, the project team conducted electricity consumption campaign. Thirteen households collaborated with the project team helping them collect their electricity consumption data over different timescales.  All households’ residents were informed about how the acquisition system works and how households can monitor their own electricity consumption using a smart-phone app or via the website. Following data acquisition, a consumption report was given to the household’s residents containing statistics and insights about their consumption behaviors. The collected data was uploaded to Africa's first open database of electricity consumption in buildings, called MORED which is open for access and use by the research community. The data can be used to size battery-backed PV systems for buildings in urban areas.

During the remaining months of the project, the PEER team plans to complete the following tasks: (1) Continuation of the data acquisition campaign in more Moroccan households  to encompass a more diverse and statistically significant dataset. Five additional households located in Kenitra and Tetuan will be monitored; (2) Investigation of the use of time series transformers to improve the performance of deep learning models for the task of multi-label load identification; (3) Update of the testbed by including gel batteries; (4)  Submission of research results for publication.
Publications and proceedings

• Mohamed Aymane Ahajjam, Daniel Bonilla Licea, Mounir Ghogho, Abdellatif Kobbane: IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings. Sensors 20(4): 1048 (2020).
• MA Ahajjam, D Bonilla Licea, C Essayeh, M Ghogho, A Kobbane : MORED: A Moroccan Buildings’ Electricity Consumption Dataset, Energies 13 (24), 6737 (2020).
• Mohamed Aymane Ahajjam, Daniel Bonilla Licea, Mounir Ghogho, Abdellatif Kobbane: Electric Power Quality Disturbances Classification based on Temporal-Spectral Images and Deep Convolutional Neural Networks. IWCMC 2020: 1701-1706 (2020).

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