@proceedings {7249,
	title = {Deep Long Short-Term Memory with Separation Models for Direct Normal Irradiance Forecasting: Application to Tamanrasset, Algeria},
	journal = {The First International Conference on Renewable Energy Advanced Technologies and Applications},
	year = {2021},
	address = {Adrar, Algeria},
	abstract = {Solar energy is a vast and clean resource that can be harnessed with great benefit for humankind. It is still currently difficult, however, to convert it into electricity in an efficient and cost-effective way. One of the ways to produce energy is the use of various focusing technologies that concentrate the direct normal irradiance (DNI) to produce power through highly-efficient modules or conventional turbines. Concentrating technologies have great potential over arid areas, such as Northern Africa. A serious issue is that DNI can vary rapidly under broken-cloud conditions, which complicate its forecasts [1]. In comparison, the global horizontal irradiance (GHI) is much less sensitive to cloudiness. As an alternative to the direct DNI forecasting avenue, a possibility exists to derive the future DNI indirectly by forecasting GHI first, and then use a conventional separation model to derive DNI. In this context, the present study compares four of the most well-known separation models of the literature and evaluates their performance at Tamanrasset, Algeria, when used in combination with a new deep learning machine methodology introduced here to forecast GHI time series for short-term horizons (15-min). The proposed forecast system is composed of two separate blocs. The first bloc seeks to forecast the future value of GHI based on historical time series using the Long Short-Term Memory (LSTM) technique with two different search algorithms. In the second bloc, an appropriate separation (also referred to as {\textquotedblleft}diffuse fraction{\textquotedblright} or {\textquotedblleft}splitting{\textquotedblright}) model is implemented to extract the direct component of GHI. LSTMs constitute a category of recurrent neural network (RNN) structure that exhibits an excellent learning and predicting ability for data with time-series sequences [2]. The present study uses and evaluates the performance of two novel and competitive strategies, which both aim at providing accurate short-term GHI forecasts: Unidirectional LSTM (UniLSTM) and Bidirectional LSTM (BiLSTM). In the former case, the signal propagates backward or forward in time, whereas in the latter case the learning algorithm is fed with the GHI data once from beginning to the end and once from end to beginning. One goal of this study is to evaluate the overall advantages and performance of each strategy. Hence, this study aims to validate this new approach of obtaining 15- min DNI forecasts indirectly, using the most appropriate separation model. An important step here is to determine which model is suitable for the arid climate of Tamanrasset, a high-elevation site in southern Algeria where dust storms are frequent. Accordingly, four representative models have been selected here, based on their validation results [3] and popularity: 1) Erbs model [4]; 2) Maxwell{\textquoteright}s DISC model [5]; 3) Perez{\textquoteright}s DIRINT model [6]; and 4) Engerer2 model [7]. In this contribution, 1-min direct, diffuse and global solar irradiance measurements from the BSRN station of Tamanrasset are first quality-controlled with usual procedures [3, 8] and combined into 15-min sequences over the period 2013{\textendash}2017. The four separation models are operated with the 15-min GHI forecasts obtained with each LSTM model, then compared to the 15-min measured DNI sequences. Table 1 shows the results obtained by the two forecasting strategies, for the experimental dataset.},
	url = {https://www.researchgate.net/profile/Abderrezzaq-Ziane/publication/357687664_ICREATA{\textquoteright}21_Proceedings/links/61daf088b8305f7c4b3195cf/ICREATA21-Proceedings.pdf},
	author = {Hanane Atmani and Hassen Bouzgou and Christian A Gueymard}
}
@proceedings {7250,
	title = {Sample Entropy with One-Stage Variational Mode Decomposition for Hourly Solar Irradiance Forecasting},
	journal = {The First International Conference on Renewable Energy Advanced Technologies and Applications},
	year = {2021},
	address = {Adrar, Algeria},
	abstract = {Solar radiation forecasting is an important technology that is necessary to increase the performance, management, and control of modern electrical grids. It allows energy regulators to estimate the near-future output power of solar power plants, and can help to reduce the effects of power fluctuations on the electricity grid, thus increasing the overall efficiency and power quality of those plants [1]. However, the variable nature of solar irradiance poses a challenge in the exploitation of solar energy. In this context, forecasting techniques are now essential to ensure sustainable, reliable, and cost-effective solar energy production [2]. This paper proposes a hybrid machine learning model to forecast Global Horizontal Irradiance (GHI) in the short term (1-hour ahead). The experimental assessment of the model is done on the basis of an experimental dataset of 11 years of hourly GHI measurements from the BSRN Tamanrasset station in Algeria. The general framework of the proposed model is explained in Figure 1, and its main steps are summarized as follows:},
	url = {https://www.researchgate.net/profile/Abderrezzaq-Ziane/publication/357687664_ICREATA{\textquoteright}21_Proceedings/links/61daf088b8305f7c4b3195cf/ICREATA21-Proceedings.pdf},
	author = {Nahed zemouri and Hassen Bouzgou and Christian A Gueymard}
}
@article {63492,
	title = {Fast short-term global solar irradiance forecasting with wrapper mutual information},
	journal = {Renewable energyRenewable energy},
	volume = {133},
	year = {2019},
	month = {2019},
	pages = {1055-1065},
	isbn = {0960-1481},
	author = {Hassen Bouzgou and Christian A Gueymard}
}
@article {63185,
	title = {Multimodel ensemble approach for hourly global solar irradiation forecasting},
	journal = {The European Physical Journal PlusThe European Physical Journal Plus},
	volume = {134},
	year = {2019},
	month = {2019},
	pages = {594},
	isbn = {2190-5444},
	author = {Nahed zemouri and Hassen Bouzgou and Christian A Gueymard}
}
