TReal World Solar Power Prediction and Use Optimisation

随着可再生能源变得越来越普遍，由于其可变和埋藏的性质，及时准确的产量预测对于有效利用它们将变得更加重要。不准确的预测输出可能会导致发电机偏离其对电网的承诺。为了维持稳定性，电网运营商被迫进行干预，采取行动来平衡电网。这一行动的成本，平衡成本，被转嫁到违规生产者身上。由于网格在短期内通常以30分钟的时间段进行交易，因此只有几个小时的预测就足以产生影响。在太阳能的情况下，发电量是可变的，在中午达到峰值。功率输出的主要来源变化是福尔斯落在发电机上的太阳辐照量。影响辐照度的一些最大因素是大气/气象。准确了解天气、云的位置、密度、移动方向和速度将有助于创建预测太阳辐照度模型。今天，商业太阳能发电场正在不断产生大量的数据。精确测量太阳辐照度，功率输出以及每个组件的详细遥测。这相当于每个工厂每天产生100，000个数据点。数据的数量和速度可能需要使用Apache Spark等大数据工具。如前所述，太阳能发电的一个主要因素是天气。商业提供商声称可以为地球上几乎任何一点提供准确的观测。人们还可以访问丰富的雷达和卫星图像。这种类型的数据很适合机器学习（ML）。有许多有趣的机器学习方法已经被证明可以利用这种数据的顺序属性来进行预测。在文献中有许多人应用ML技术来预测太阳辐照度的例子。Alzahrani等人已经证明了深度学习的使用，以递归神经网络的形式，预测太阳辐照度[1]。Marquez等人使用气象数据和人工神经网络预测太阳辐照度长达6天[2]。各种优化技术也存在于文献中的广泛领域。Xue等人描述了一种基于模拟的易腐食品优化方法，在确保足够供应以满足客户需求的同时最大限度地减少腰围[3]。研究的主要目的是开发新的大数据学习和优化技术。特别是对于预测的实际应用，任意大小的太阳能电池阵列（家用或商用）的功率输出和优化阵列的功率使用（存储/销售到电网）。具体来说，这将涉及：功率预测：设计和开发一个模型来预测太阳辐照度。设计和开发一个系统，用于预测辐照度并转换为给定太阳能电池阵列的功率输出。电力优化：设计和开发一个系统来建模/预测全天的电力使用和价格。设计和开发一个系统，以优化如何使用来自太阳能电池阵列的电力符合各种约束（例如，电池充电周期，电网购买/销售价格，预测未来的功率输出）。参考文献[1] Ahmad Alzahrani，Pourya Shamsi，Cihan Dagli，and Mehdi Ferdowsi.使用深度神经网络预测太阳辐照度。Procedia Computer Science，114：304 - 313，2017。[2]里卡多·马奎兹和卡洛斯F.M.科英布拉。使用随机学习方法、地面试验和NWS数据库预报全球和直接太阳辐照度。Solar Energy，85（5）：746 - 756，2011. [3]宁雪，达里奥·兰达-席尔瓦，格拉齐埃拉·菲格雷多，艾萨克·特里格罗。以模拟为基础之高度易腐食品存货管理最佳化方法。2019年02月。

As renewable energy sources become more prevalent, due to their variable and interment nature, having a timely and accurate forecast of production will be ever more vital for their effective use. Inaccurately forecasting output can cause the generator to deviate from their commitment to the grid. In order to maintain stability the grid operator is forced to intervene, taking action to balance the grid. The cost of this action, the balancing cost, is passed onto the offending producer. Since the grid, in the short run, usually trades in 30 minute blocks having a prediction only a few hours into the future can be enough to make an impact. In the case of solar, generation is variable and peaks during the middle of the day. The main source variability in power output is the amount of solar irradiance that falls on the generator. Some of the biggest factors that effect irradiance are atmospheric / meteorological. Having an accurate picture of the weather, cloud location, density, direction and speed of movement would help facilitate the creation of a predictive solar irradiance model. Today commercial solar farms are continuously producing vast amounts of data. Accurate measures of solar irradiance, power output as well as detailed telemetry for each component. This can amount to 100,000s of data points per plant being generated every day. The volume and velocity of data can necessitate the use of big data tools such as Apache Spark. As previously stated, a major factor in solar production is the weather. Commercial providers claim to offer accurate observations for virtually any point on the planet. One can also access rich radar and satellite imagery. This type of data lends itself well to machine learning (ML). There are many interesting ML approaches that have been show to work leveraging the sequential properties of this kind of data to make predictions. There are many examples in the literature of people applying ML techniques to predict solar irradiance. Alzahrani et al have demonstrated use of deep learning, in the form of a recurrent neural network, to forecast solar irradiance [1]. Marquez et al have used meteorological data and an ANN to predict solar irradiance up to 6 days out [2]. Various optimisation techniques also exist within the literature for a broad range of domains. Xue et al describe a simulation-based optimisation for perishable food, minimising waist whist ensuring enough supply to meet customer demand [3]. Aims and Objectives The main aim of the research is to develop novel big data learning and optimisation techniques. Specifically for the practical application of predicting, power output for arbitrary sized solar arrays (either domestic or commercial) and optimising power use from the array (storage / selling to the grid). Specifically this will involve: Power Prediction: Design and develop a model to predict solar irradiance. Design and develop a system to take irradiance predictions and convert to power output for a given solar array. Power Optimisation: Design and develop a system to model / predict power use and prices throughout the day. Design and develop a system to optimise how power from the solar array is used conforming to various constrains (e.g battery charge cycles, gird buy / sell prices, predicted future power output).References [1] Ahmad Alzahrani, Pourya Shamsi, Cihan Dagli, and Mehdi Ferdowsi. Solar irradiance forecasting using deep neural networks. Procedia Computer Science, 114:304 - 313, 2017. [2] Ricardo Marquez and Carlos F.M. Coimbra. Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database. Solar Energy, 85(5):746 - 756, 2011. [3] Ning Xue, Dario Landa-Silva, Grazziela Figueredo, and Isaac Triguero. A simulation-based optimisation approach for inventory management of highly perishable food. 02 2019.