Regional solar power forecasting, which involves predicting the total power generation from all rooftop photovoltaic systems in a region holds significant importance for various stakeholders in the energy sector. However, the vast amount of solar power generation and weather time series from geographically dispersed locations that need to be considered in the forecasting process makes accurate regional forecasting challenging. Therefore, previous work has limited the focus to either forecasting a single time series (i.e., aggregated time series) which is the addition of all solar generation time series in a region, disregarding the location-specific weather effects or forecasting solar generation time series of each PV site (i.e., individual time series) independently using location-specific weather data, resulting in a large number of forecasting models. In this work, we propose two deep-learning-based regional forecasting methods that can effectively leverage both types of time series (aggregated and individual) with weather data in a region. We propose two hierarchical temporal convolutional neural network architectures (HTCNN) and two strategies to adapt HTCNNs for regional solar power forecasting. At first, we explore generating a regional forecast using a single HTCNN. Next, we divide the region into multiple sub-regions based on weather information and train separate HTCNNs for each sub-region; the forecasts of each sub-region are then added to generate a regional forecast. The proposed work is evaluated using a large dataset collected over a year from 101 locations across Western Australia to provide a day ahead forecast. We compare our approaches with well-known alternative methods and show that the sub-region HTCNN requires fewer individual networks and achieves a forecast skill score of 40.2% reducing a statistically significant error by 6.5% compared to the best counterpart.

Pune, India, Sept. 16, 2021 (GLOBE NEWSWIRE) -- The global Artificial Intelligence (AI) market size is expected to gain momentum by reaching USD 360.36 billion by 2028 while exhibiting a CAGR of 33.6% between 2021 to 2028. In its report titled, "Artificial Intelligence (AI) Market Size, Share & COVID-19 Impact Analysis, By Component (Hardware, Software, and Services), By Technology (Computer Vision, Machine Learning, Natural Language Processing, and Others), By Deployment (Cloud, On-premises), By Industry (Healthcare, Retail, IT & Telecom, BFSI, Automotive, Advertising & Media, Manufacturing, and Others), and Regional Forecast, 2021-2028" Fortune Business Insights mentions that the market stood at USD 35.92 billion in 2020. Artificial Intelligence has become immensely popular, and industries across the globe are rapidly incorporating it into their processes to improve business operations and customer experience. Not only the big companies but also the small and medium businesses are investing in this technology. Besides, the advancement and implementation of 5G, cloud computing, and a huge database are the factors, which are propelling its demand.

Key Companies Covered in NLP Market Research Report are 3M Company, Adobe Systems Inc., Amazon Web Services Inc., Apple Inc., Google (Alphabet Inc.), Hewlett-Packard Enterprise Company, Intel Corporation, Microsoft Corporation, SAS Institute Inc., Other key market players The global Natural Language Processing (NLP) Market size is projected to reach USD 80.68 billion by 2026, thereby exhibiting a CAGR of 32.4% during the forecast period. This information is published by Fortune Business Insights, in a report, titled, "Natural Language Processing (NLP) Market Size, Share & Industry Analysis, By Deployment (On-Premises, Cloud, and Hybrid), By Technology (Interactive Voice Response (IVR), Optical Character Recognition (OCR), Text Analytics, Speech Analytics, Classification and Categorization, Pattern and Image Recognition, and Others), By Industry Vertical (Healthcare, Retail, High Tech and Telecom, BFSI, Automotive & Transportation, Advertising & Media, Manufacturing, and Others) and Regional Forecast, 2019-2026." The report further states that the market was USD 8.61 billion in 2018. It is set to gain momentum from the rising demand for big data, improved algorithms, and powerful computing. What Does the Report Contain?