Thailand has targeted to achieve carbon neutrality by 2050 when the power grid will need to accommodate 50% share of renewable electricity generation capacity; see [Ene21]. The most recent draft of Power Development Plan 2024 (PDP2024) for 2024 - 2037 from [Ene24] proposes to add a new solar generation capacity of approximately 24,400 MWp (more than 4 times the amount issued in the previous Alternative Energy Development Plan 2015-2036 (AEDP2015) at 6,000 MWp, shown in [Dep15, p.9]. This amount does not yet include behind-the-meter, self-generation solar installed capacities of the prosumers, which is expected to increase at an accelerating rate. Solar integration into the power grid with such a sharprising amount will pose technical challenges to the operation and control of the transmission and distribution networks, carried out by the transmission system operator (TSO) and distribution system operator (DSO), as presented in [OB16]. Hence, TSO in Thailand will need an effective means to estimate the solar power generation across the entire transmission network, on an hourly basis, or even finer time resolution, to provide economic hour-to-hour generation dispatch for load following the total net load of the transmission, and to prepare sufficient system flexibility (i.e., ramp-rate capability of the thermal and hydropower plants, or energy storage systems) to cope with the net load fluctuation due to solar generation intermittency for maintaining system frequency stability, concurrently, in its operation. For DSO, a significant amount of reverse power flow when self-generation from solar exceeds self-consumption can lead to technical concerns of voltage regulation and equipment overloading problems. The near real-time estimation of solar generation in each distribution area will enable DSO to activate proper network switching or reconfiguring to mitigate such fundamental concerns to ensure its reliable operation.

Pre-trained Language Models (PLMs) encode various facts about the world at their pre-training phase as they are trained to predict the next or missing word in a sentence. There has a been an interest in quantifying and improving the amount of facts that can be extracted from PLMs, as they have been envisioned to act as soft knowledge bases, which can be queried in natural language. Different approaches exist to enhance fact retrieval from PLM. Recent work shows that the hidden states of PLMs can be leveraged to determine the truthfulness of the PLMs' inputs. Leveraging this finding to improve factual knowledge retrieval remains unexplored. In this work, we investigate the use of a helper model to improve fact retrieval. The helper model assesses the truthfulness of an input based on the corresponding hidden states representations from the PLMs. We evaluate this approach on several masked PLMs and show that it enhances fact retrieval by up to 33\%. Our findings highlight the potential of hidden states representations from PLMs in improving their factual knowledge retrieval.

MEPs have warned European countries must "seriously" consider introducing a general basic income to prepare for wide scale unemployment that could come as a result of robots taking over manual jobs. A draft report, tabled by a socialist MEP Mady Delvaux-Stehres, warns preparations must be made for what it describes as the "technological revolution" currently taking place, including provisions for the "possible effects on the labour market of robotics". The report, which passed by 17 votes to two and will be put in front of the entire European Parliament in February, urges member states to consider a general basic income in preparation for robots taking over people's jobs. It states: "In the light of the possible effects on the labour market of robotics and AI a general basic income should be seriously considered, and invites all Member States to do so." The resolution also suggests that a system of reporting on how robotics are affecting the economic results of companies should be established "for the purpose of taxation and social security contributions".