The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and resulting in increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon solar forecasting for short and long-term predictions using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid. Today's power grid has become dynamic in nature mainly because of three changes in the modern grid: 1. Higher penetration level of renewables, 2. Introduction (and rapidly increasing deployment) of storage devices, and 3. Loads becoming active (by participating in demand response). This dynamic modern grid faces the challenge of strong fluctuations due to uncertainty. There is a critical need of gaining real time observability, control, and improving renewable generation forecast accuracy to enhance the resiliency and keep the operational costs sustainable. Independent system operators (ISOs) with higher renewable penetration on the grid have already been facing challenges with the uncertainties associated with short-term forecasting errors. In year 2016, California ISO doubled its frequency regulation service requirements (causing a sharp rise in the cost of requirements) to manage the recurring short-term forecasting errors in renewable generation BID0 . The Western Electricity Coordinating Council (WECC) could achieve $5 billion savings per year by integrating wind and solar forecasts into unit commitment, according to the study conducted by Lew et al BID1 . Thus, it is clear that the increased grid penetration levels of solar with its inherent variability (a combination of intermittence, high-frequency and non-stationarity) poses problems with grid reliability and cost of operating the grid on various time-scales. For example, day-ahead solar forecast accuracy plays a significant role in the effectiveness of Unit Commitment (UC); very-short-term solar forecasts errors due to fluctuations caused by the passing clouds lead to sudden changes in PV plant outputs that can cause strain to the grid by inducing voltage-flickers and real-time balancing issues. Thus, solar power generation forecast becomes an area of paramount research, as the need for robust forecast for all timescales (weekly, day-ahead, hourly and intra-hour) is critical for effectively incorporating increasing amount of solar energy resources at a global level and contributing to the evolution of the smart grid. Moreover, improving the accuracy of solar forecast is one of the lowest cost methods of efficiently integrating solar energy into the grid.The rest of the paper is organized as follows. The literature is reviewed and the significant shortcomings of the current forecasting approaches are recognized in Section II. Section II further introduces the capabilities of the proposed unified architecture and the novel algorithm to fill in the gap between the need to improve the forecasting techniques and the existing approaches. Section III introduces the proposed unified architecture based on RNN and the training algorithms utilized for implementing the neural network. Exploratory data analysis, evaluation metric and structure of input data, and the proposed algorithm are presented in Section IV. Section V discusses the results and their interpretation. The paper is concluded with Section VI, which also identifies the future avenue of research in this method of solar forecasting.. The algorithm is trained using the data for the year 2010 and 2011 from the SURFRAD observations sites in Boulder, CO; Desert Rock, NV; Fort Peck, MT; Sioux Falls, SD; Bondville, IL; Goodwin Creek, MS; and Penn State, PA. The test year for each respective site was chosen to be 2009 for the purpose of benchmarking against BID28 and other previously reported results in the literature. Results from the two methods proposed in this paper are presented below: Short-term solar forecasting is of great importance for optimizing the operational efficiencies of smart grids, as the uncertainties in the power systems are ever-increasing, spanning from the generation arena to the demand-side domain. A number of methods and applications have been developed for solar forecasting, with some level of predictive success. The main limitation of the approaches developed so far is their specificity with a given temporal and/or spatial resolution. For predictive analysis problems, the field of AI has become promising with the recent advances in optimization techniques, parallelism, and GPUs. AI (especially deep neural networks) thrives on data, and with decreasing cost of sensor and measurement equipment, plethora of solar data is getting available. Data availability is only going to keep increasing in the coming years. The proposed novel Unified Recurrent Neural Network Architecture harnesses the power of AI to form a high-fidelity solar forecasting engine. This architecture has the potential to be implemented as a complete forecasting system, which spans the entire spectrum of spatial and temporal horizons with a capability to take real-time data as input to produce multi-time-scale (intra-hour, hourly and day-ahead scales) predictions. In addition, the proposed algorithm outperforms traditional Machine Learning methods in terms of quality of the forecast and its low forward inference time makes it a robust real-time solar forecasting engine.Although a deeper neural network will have more capacity, we experimentally observed that it leads to high variance in the model and therefore a reduced generalization power for the particular problem dealt in this paper. The performance of the proposed method can be further improved in several ways including hyper-parameter tuning and architectural changes like the activation functions used or the type of layers. Extension of the proposed architecture with LSTM cells and intra-hour forecasting horizons are potential future research avenues in this domain.
This paper proposes a Unified Recurrent Neural Network Architecture for  short-term multi-time-horizon solar forecasting and validates the forecast performance gains over the previously reported methods
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