TReal World Solar Power Prediction and Use Optimisation

随着可再生能源变得越来越普遍，由于其多变性和埋藏性，及时和准确地预测产量将对其有效利用变得更加重要。不准确的发电量预测会导致发电商背离对电网的承诺。为了维持稳定，电网运营商被迫进行干预，采取行动平衡电网。这一行动的成本，即平衡成本，被转嫁到违规的生产商身上。由于电网在短期内通常以30分钟的区块进行交易，因此只需几个小时的预测就足以产生影响。就太阳能而言，发电量是多变的，并在中午达到峰值。功率输出的主要来源可变性是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/气象因素。对天气、云的位置、密度、方向和移动速度有一张准确的图像将有助于建立一个预测性的太阳辐射模型。今天，商业太阳能发电场正在源源不断地产生海量数据。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。数据量和速度可能需要使用大数据工具，如ApacheSpark。如前所述，影响太阳能生产的一个主要因素是天气。商业提供商声称可以为地球上的几乎任何一个点提供准确的观测。人们还可以访问丰富的雷达和卫星图像。这种类型的数据非常适合机器学习(ML)。有许多有趣的ML方法已经被证明是有效的，它们利用这种数据的顺序属性来进行预测。在文献中有许多应用ML技术来预测太阳辐照度的例子。阿尔扎赫拉尼等人展示了以递归神经网络的形式使用深度学习来预测太阳辐照度[1]。Marquez等人使用气象数据和人工神经网络来预测长达6天的太阳辐射[2]。各种优化技术也存在于广泛领域的文献中。薛等人描述了一种基于模拟的易腐烂食品的优化，最大限度地减少了腰围，确保了足够的供应来满足客户的需求[3]。目的和目标这项研究的主要目的是开发新的大数据学习和优化技术。专门用于预测任意尺寸的太阳能电池板(无论是家用还是商用)的功率输出和优化阵列的电力使用(存储/出售给电网)的实际应用。具体来说，这将涉及：功率预测：设计和开发一个预测太阳辐照度的模型。设计和开发一个系统，用于获取辐照度预测并转换为给定太阳能电池板的功率输出。电力优化：设计和开发一个系统，对全天的电力使用和价格进行建模/预测。设计和开发一个系统，以优化太阳能电池阵列的电力使用方式，符合各种约束(例如电池充电周期、网格买卖价格、预测未来功率输出)。参考文献[1]Ahmad阿尔扎拉尼、Pourya Shamsi、Cihan Dagli和Mehdi Ferdowsi。基于深度神经网络的太阳辐照度预测。Procedia Computer Science，114：304-313,2017。[2]里卡多·马克斯和卡洛斯·F·M·科英布拉。使用随机学习方法、地面实验和自然资源数据库预报太阳总辐射量和直接辐射量。太阳能，85(5)：746-756,2011。[3]宁雪、达里奥·兰达-席尔瓦、格拉齐埃拉·菲格雷多和艾萨克·特里奎罗。一种基于模拟的高度易腐烂食品库存管理优化方法。2019年2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.